feat: initial commit — CustomerCore Phases 1-9 complete

- Phase 1: Project scaffold, Docker Compose (6 services), Doppler secrets
- Phase 2: Redpanda streaming — 4 event producers (tickets, billing, incidents, products)
- Phase 3: PySpark Bronze→Silver pipeline, Presidio PII masking, MinIO lakehouse
- Phase 4: dbt Gold analytics layer — 7 business marts, 18 data contract tests, DuckDB
- Phase 5: Cryptographic Privacy Vault — AES-256-GCM, RBAC, GDPR Article 17 right-to-erasure
- Phase 6: Multi-tenant Hybrid RAG — ChromaDB + BM25 + RRF fusion + 3-layer semantic cache
- Phase 7: SLA-aware multi-model LLM router — LiteLLM, OpenRouter, 54% local routing
- Phase 8: Multi-language support (7 langs, 87k rows) + Unified B2B Graph-RAG (NetworkX)
- Phase 9: LangGraph 6-agent supervisor — HITL, MemorySaver checkpointing, 223 tests

Stack: Python 3.12, Redpanda, MinIO, ChromaDB, Redis, DuckDB, dbt, LangGraph, LiteLLM
Dataset: 52,417 real-world support tickets (Bitext EN + Amazon MASSIVE DE/FR/ES/PT/IT/NL/JA)

.env.example

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+# CustomerCore Environment Variables
+# All real values are managed via Doppler — never put real secrets here
+# Run: doppler setup && doppler run -- <your-command>
+
+APP_ENV=development
+
+# --- LLM APIs ---
+OPENROUTER_API_KEY=your_openrouter_key_here
+LITELLM_MASTER_KEY=sk-your-litellm-key-here
+
+# --- Supabase (PostgreSQL + Auth + pgvector) ---
+SUPABASE_URL=https://your-project-ref.supabase.co
+SUPABASE_ANON_KEY=your_anon_key_here
+SUPABASE_SERVICE_ROLE_KEY=your_service_role_key_here
+SUPABASE_DB_URL=postgresql://postgres:your-password@db.your-project-ref.supabase.co:5432/postgres
+SUPABASE_DB_PASSWORD=your_password_here
+SUPABASE_PROJECT_REF=your_project_ref_here
+
+# --- Redis (local dev via Docker, cloud via Upstash) ---
+UPSTASH_REDIS_URL=rediss://default:your_token@your-host.upstash.io:6379
+
+# --- MLflow / DagsHub ---
+MLFLOW_TRACKING_URI=https://dagshub.com/username/CustomerCore.mlflow
+DAGSHUB_TOKEN=your_dagshub_token_here
+
+# --- Observability ---
+LANGFUSE_PUBLIC_KEY=pk-lf-your-key-here
+LANGFUSE_SECRET_KEY=sk-lf-your-key-here
+SENTRY_DSN=https://your-sentry-dsn@sentry.io/project-id
+
+# --- (Phase 18) Cloud Storage ---
+R2_ACCOUNT_ID=your_cloudflare_account_id
+R2_ACCESS_KEY_ID=your_r2_access_key
+R2_SECRET_ACCESS_KEY=your_r2_secret_key
+R2_BUCKET_NAME=customercore-lake

.gitattributes

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+# Normalize line endings for all text files
+* text=auto eol=lf
+
+# Python files
+*.py text eol=lf
+*.pyi text eol=lf
+
+# Config files
+*.yml text eol=lf
+*.yaml text eol=lf
+*.toml text eol=lf
+*.json text eol=lf
+*.sql text eol=lf
+*.md text eol=lf
+
+# Binary files
+*.png binary
+*.jpg binary
+*.gif binary
+*.ico binary
+*.docx binary
+*.db binary
+*.duckdb binary

.gitignore

@@ -0,0 +1,49 @@
+.venv/
+__pycache__/
+*.pyc
+*.pyo
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+
+# Secrets - NEVER commit
+.env
+.env.local
+.env.*.local
+.doppler.yaml
+*.key
+*.pem
+
+# Data and databases
+data/
+*.duckdb
+*.db
+mlruns.db
+mlruns/
+
+# dbt artifacts
+.dbt/
+dbt_packages/
+target/
+
+# Logs and audit trails
+logs/
+*.log
+audit_trail.jsonl
+
+# Old version files
+customercore_v*.docx
+customercore_v*.js
+
+# IDE
+.vscode/
+.idea/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Coverage
+htmlcov/
+.coverage
+coverage.xml

docker-compose.yml

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+version: "3.8"
+
+# ============================================================
+# CustomerCore Local Infrastructure Stack
+# Start:  docker compose up -d
+# Stop:   docker compose down
+# Logs:   docker compose logs -f <service>
+# ============================================================
+
+networks:
+  customercore:
+    driver: bridge
+
+volumes:
+  redpanda_data:
+  minio_data:
+  chroma_data:
+  redis_data:
+
+services:
+
+  # ----------------------------------------------------------
+  # REDPANDA — Kafka-compatible streaming broker
+  # Replaces Kafka with no JVM, no ZooKeeper, single binary
+  # Broker listens on: localhost:9092 (internal: 29092)
+  # ----------------------------------------------------------
+  redpanda:
+    image: docker.redpanda.com/redpandadata/redpanda:latest
+    container_name: customercore_redpanda
+    command:
+      - redpanda
+      - start
+      - --kafka-addr internal://0.0.0.0:29092,external://0.0.0.0:9092
+      - --advertise-kafka-addr internal://redpanda:29092,external://localhost:9092
+      - --pandaproxy-addr internal://0.0.0.0:28082,external://0.0.0.0:8082
+      - --advertise-pandaproxy-addr internal://redpanda:28082,external://localhost:8082
+      - --schema-registry-addr internal://0.0.0.0:28081,external://0.0.0.0:8081
+      - --rpc-addr redpanda:33145
+      - --advertise-rpc-addr redpanda:33145
+      - --mode dev-container
+      - --smp 1
+      - --default-log-level=warn
+    volumes:
+      - redpanda_data:/var/lib/redpanda/data
+    networks:
+      - customercore
+    ports:
+      - "9092:9092"    # Kafka API — Python producers connect here
+      - "8081:8081"    # Schema Registry
+      - "8082:8082"    # HTTP Proxy (Pandaproxy)
+    healthcheck:
+      test: ["CMD-SHELL", "rpk cluster health | grep -E 'Healthy|true' || exit 1"]
+      interval: 15s
+      timeout: 10s
+      retries: 5
+
+  # ----------------------------------------------------------
+  # REDPANDA CONSOLE — Visual web dashboard for Redpanda
+  # Browse topics, inspect messages, debug producers
+  # Dashboard: http://localhost:8080
+  # ----------------------------------------------------------
+  console:
+    image: docker.redpanda.com/redpandadata/console:latest
+    container_name: customercore_console
+    environment:
+      CONFIG_FILEPATH: /etc/redpanda/redpanda-console-config.yaml
+    entrypoint: /bin/sh
+    command: >
+      -c 'printf "kafka:\n  brokers: [\"redpanda:29092\"]\n" > /etc/redpanda/redpanda-console-config.yaml && /app/console'
+    networks:
+      - customercore
+    ports:
+      - "8080:8080"    # Web dashboard
+    depends_on:
+      - redpanda
+
+  # ----------------------------------------------------------
+  # MINIO — S3-compatible local object storage
+  # Stores Apache Iceberg Parquet data files
+  # Console: http://localhost:9001 (admin/password: minioadmin)
+  # API:     http://localhost:9000
+  # ----------------------------------------------------------
+  minio:
+    image: minio/minio:latest
+    container_name: customercore_minio
+    command: server /data --console-address ":9001"
+    environment:
+      MINIO_ROOT_USER: minioadmin
+      MINIO_ROOT_PASSWORD: minioadmin
+    volumes:
+      - minio_data:/data
+    networks:
+      - customercore
+    ports:
+      - "9000:9000"    # S3 API
+      - "9001:9001"    # MinIO web console
+    healthcheck:
+      test: ["CMD", "mc", "ready", "local"]
+      interval: 30s
+      timeout: 20s
+      retries: 3
+
+  # ----------------------------------------------------------
+  # CHROMADB — Vector database for RAG retrieval
+  # Stores BGE-M3 embeddings of support tickets and KB articles
+  # API: http://localhost:8000
+  # ----------------------------------------------------------
+  chromadb:
+    image: chromadb/chroma:latest
+    container_name: customercore_chromadb
+    volumes:
+      - chroma_data:/chroma/chroma
+    networks:
+      - customercore
+    ports:
+      - "8000:8000"    # ChromaDB HTTP API
+    environment:
+      - IS_PERSISTENT=TRUE
+      - ALLOW_RESET=TRUE
+
+  # ----------------------------------------------------------
+  # REDIS — In-memory cache and rate limiter
+  # L1 exact-match semantic cache + per-tenant rate limiting
+  # ----------------------------------------------------------
+  redis:
+    image: redis:alpine
+    container_name: customercore_redis
+    command: redis-server --appendonly yes --maxmemory 256mb --maxmemory-policy allkeys-lru
+    volumes:
+      - redis_data:/data
+    networks:
+      - customercore
+    ports:
+      - "6379:6379"    # Redis default port
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # ----------------------------------------------------------
+  # OPENTELEMETRY COLLECTOR — Central telemetry pipeline
+  # Scrapes Prometheus metrics from FastAPI /metrics endpoint
+  # Forwards to Grafana Cloud (metrics, logs, traces)
+  # ----------------------------------------------------------
+  otel-collector:
+    image: otel/opentelemetry-collector-contrib:latest
+    container_name: customercore_otel
+    volumes:
+      - ./infra/otel-collector.yaml:/etc/otelcol-contrib/config.yaml
+    networks:
+      - customercore
+    ports:
+      - "4317:4317"    # OTLP gRPC receiver
+      - "4318:4318"    # OTLP HTTP receiver
+      - "8888:8888"    # OTel internal metrics
+    depends_on:
+      - redpanda

infra/otel-collector.yaml

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+# ============================================================
+# OpenTelemetry Collector Configuration
+# Receives: OTLP from FastAPI app
+# Scrapes:  Prometheus /metrics from FastAPI
+# Exports:  To Grafana Cloud (configured later in Phase 13)
+#           For now: logs everything to console for local dev
+# ============================================================
+
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: 0.0.0.0:4317
+      http:
+        endpoint: 0.0.0.0:4318
+
+  prometheus:
+    config:
+      scrape_configs:
+        - job_name: "customercore-api"
+          scrape_interval: 15s
+          static_configs:
+            - targets: ["host.docker.internal:8001"]
+
+processors:
+  batch:
+    timeout: 10s
+  memory_limiter:
+    check_interval: 1s
+    limit_mib: 256
+
+exporters:
+  debug:
+    verbosity: normal
+
+service:
+  pipelines:
+    metrics:
+      receivers: [prometheus, otlp]
+      processors: [memory_limiter, batch]
+      exporters: [debug]
+    traces:
+      receivers: [otlp]
+      processors: [memory_limiter, batch]
+      exporters: [debug]
+    logs:
+      receivers: [otlp]
+      processors: [memory_limiter, batch]
+      exporters: [debug]

pyproject.toml

@@ -0,0 +1,27 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "customercore"
+version = "1.0.0"
+description = "CustomerCore Intelligence Platform"
+requires-python = ">=3.11"
+
+[tool.pytest.ini_options]
+minversion = "7.0"
+addopts = "-ra -q --tb=short"
+testpaths = [
+    "tests",
+]
+pythonpath = [
+    "."
+]
+
+[tool.ruff]
+line-length = 120
+target-version = "py311"
+
+[tool.mypy]
+python_version = "3.11"
+ignore_missing_imports = true

src/agent/memory.py

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+import os
+import structlog
+from typing import Optional, List
+
+log = structlog.get_logger()
+
+SUPABASE_DB_URL = os.environ.get("SUPABASE_DB_URL", "")
+SUPABASE_DB_HOST = os.environ.get("SUPABASE_DB_HOST", "")
+
+_mem0_client: Optional[any] = None
+# In-memory storage for local dev / fallback when Supabase is down
+_local_memory_store: dict[str, list[str]] = {}
+
+def get_mem0_client():
+    """Get the Mem0 client, or raise Exception if not configured/available."""
+    global _mem0_client
+    if _mem0_client is None:
+        if not SUPABASE_DB_HOST:
+            raise ValueError("SUPABASE_DB_HOST is not configured. Falling back to local in-memory store.")
+            
+        from mem0 import Memory
+        config = {
+            "vector_store": {
+                "provider": "pgvector",
+                "config": {
+                    "dbname": "postgres",
+                    "user": "postgres",
+                    "password": os.environ.get("SUPABASE_DB_PASSWORD", ""),
+                    "host": SUPABASE_DB_HOST,
+                    "port": 5432,
+                    "collection_name": "memories",
+                },
+            },
+            "embedder": {
+                "provider": "ollama",
+                "config": {
+                    "model": "bge-m3",
+                    "ollama_base_url": os.environ.get("OLLAMA_URL", "http://localhost:11434"),
+                },
+            },
+            "llm": {
+                "provider": "litellm",
+                "config": {
+                    "model": "gemma-3-1b",
+                    "api_base": os.environ.get("LITELLM_URL", "http://localhost:4000"),
+                    "api_key": os.environ.get("LITELLM_MASTER_KEY", "sk-test"),
+                },
+            },
+        }
+        _mem0_client = Memory.from_config(config)
+        log.info("mem0_initialized")
+    return _mem0_client
+
+def store_memory(customer_id: str, tenant_id: str, content: str, agent_id: str = "triage_agent"):
+    """Store customer memory either in Mem0/Supabase or local in-memory store."""
+    user_id = f"{tenant_id}:{customer_id}"
+    try:
+        client = get_mem0_client()
+        client.add(
+            messages=[{"role": "user", "content": content}],
+            user_id=user_id,
+            agent_id=agent_id,
+        )
+        log.info("memory_stored", user_id=user_id, agent_id=agent_id)
+    except Exception as e:
+        # Fallback to local dict store
+        log.debug("mem0_store_failed_using_local_fallback", user_id=user_id, error=str(e))
+        if user_id not in _local_memory_store:
+            _local_memory_store[user_id] = []
+        _local_memory_store[user_id].append(content)
+        log.info("memory_stored_locally", user_id=user_id)
+
+def recall_memories(customer_id: str, tenant_id: str, query: str, limit: int = 5) -> List[str]:
+    """Recall customer memories from Mem0 or local in-memory store."""
+    user_id = f"{tenant_id}:{customer_id}"
+    try:
+        client = get_mem0_client()
+        results = client.search(query=query, user_id=user_id, limit=limit)
+        memories = [r.get("memory", "") for r in results]
+        log.info("memories_recalled", user_id=user_id, count=len(memories))
+        return memories
+    except Exception as e:
+        # Fallback to local dict search
+        log.debug("mem0_recall_failed_using_local_fallback", user_id=user_id, error=str(e))
+        local_mems = _local_memory_store.get(user_id, [])
+        # Very simple query match fallback: return matches or recent ones up to limit
+        matches = []
+        for mem in reversed(local_mems):
+            if any(word in mem.lower() for word in query.lower().split()):
+                matches.append(mem)
+            if len(matches) >= limit:
+                break
+        if not matches:
+            matches = local_mems[-limit:]
+        log.info("memories_recalled_locally", user_id=user_id, count=len(matches))
+        return matches
+
+def get_all_memories(customer_id: str, tenant_id: str) -> List[str]:
+    """Get all memories for a customer."""
+    user_id = f"{tenant_id}:{customer_id}"
+    try:
+        client = get_mem0_client()
+        results = client.get_all(user_id=user_id)
+        return [r.get("memory", "") for r in results]
+    except Exception as e:
+        log.debug("mem0_get_all_failed_using_local_fallback", error=str(e))
+        return _local_memory_store.get(user_id, [])

src/agent/nodes/churn_agent.py

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+import os
+import mlflow
+import structlog
+from typing import List
+from src.agent.state import AgentState
+
+log = structlog.get_logger()
+
+CHURN_MODEL = "customercore-churn-classifier"
+SLA_MODEL = "customercore-sla-classifier"
+
+def _load_model(name: str):
+    """Attempt to load risk model from MLflow tracking server."""
+    tracking_uri = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:///mlruns.db")
+    mlflow.set_tracking_uri(tracking_uri)
+    client = mlflow.MlflowClient()
+    try:
+        versions = client.get_latest_versions(name, stages=["Production", "None"])
+        if versions:
+            return mlflow.sklearn.load_model(f"models:/{name}/{versions[0].version}")
+    except Exception as e:
+        log.warning("model_load_failed", name=name, error=str(e))
+    return None
+
+def calculate_heuristic_risks(body: str, category: str, priority: str, tier: str) -> tuple[float, float]:
+    """
+    High-fidelity heuristic calculation of churn risk and SLA breach risk.
+    Balances customer tier, priority severity, category, and specific text sentiment cues.
+    """
+    text = body.lower()
+    
+    # ── SLA Breach Risk Calculation ────────────────────────────────────────────
+    # Baseline by priority
+    sla_base = {
+        "low": 0.10,
+        "medium": 0.25,
+        "high": 0.55,
+        "critical": 0.85
+    }
+    sla_risk = sla_base.get(priority, 0.25)
+    
+    # Enterprise or high-tier SLAs have tighter response windows, increasing breach probability
+    if tier.lower() == "enterprise":
+        sla_risk += 0.10
+    elif tier.lower() == "professional":
+        sla_risk += 0.05
+        
+    # High-pressure categories have tighter operation windows
+    if category in ["incident", "security"]:
+        sla_risk += 0.10
+    elif category == "performance":
+        sla_risk += 0.05
+        
+    # SLA risk bounds
+    sla_risk = max(0.0, min(1.0, round(sla_risk, 3)))
+    
+    # ── Churn Risk Calculation ─────────────────────────────────────────────────
+    churn_risk = 0.10  # Baseline churn risk
+    
+    # Priority additions
+    if priority == "critical":
+        churn_risk += 0.15
+    elif priority == "high":
+        churn_risk += 0.08
+        
+    # Category impact
+    if category == "billing":
+        churn_risk += 0.25  # Billing/double charging is the #1 churn driver
+    elif category == "incident":
+        churn_risk += 0.15  # Total service outages damage brand trust
+    elif category == "performance":
+        churn_risk += 0.08  # Persistent sluggishness leads to frustration
+    elif category == "security":
+        churn_risk += 0.20  # Security breaches are severe churn drivers
+        
+    # Tier impact (free users churn easily; enterprise accounts are highly valued, high-risk churn)
+    if tier.lower() == "free":
+        churn_risk += 0.10  # Low switching barriers
+    elif tier.lower() == "enterprise":
+        # While contractual barriers exist, a severe issue raises the "Account-at-Risk" flag
+        churn_risk += 0.05
+        
+    # Content-based threat indicators (direct expressions of churning intent)
+    churn_keywords = [
+        "cancel", "refund", "close my account", "switching to", "competitor",
+        "leave", "stop subscription", "unacceptable", "disappointed",
+        "useless", "moving away", "sue", "legal action"
+    ]
+    if any(kw in text for kw in churn_keywords):
+        churn_risk += 0.25
+        
+    # Churn risk bounds
+    churn_risk = max(0.0, min(1.0, round(churn_risk, 3)))
+    
+    return sla_risk, churn_risk
+
+def churn_agent_node(state: AgentState) -> AgentState:
+    ticket = state["ticket"]
+    body = ticket["body"]
+    category = state.get("category", "other")
+    priority = state.get("priority", "medium")
+    tier = ticket.get("customer_tier", "free")
+    
+    models_used: List[str] = state.get("models_used") or []
+    sla_breach_risk = None
+    churn_risk = None
+    
+    # 1. Try MLflow ML models first
+    try:
+        from src.ml.feature_engineering import create_structured_features
+        import pandas as pd
+        
+        df = pd.DataFrame([{
+            "body": body,
+            "priority": priority,
+            "customer_tier": tier,
+            "reopen_count": 0,
+            "ticket_age_hours": 24,
+        }])
+        features = create_structured_features(df)
+        
+        # Predict Churn Risk
+        churn_model = _load_model(CHURN_MODEL)
+        if churn_model:
+            # Assume probability prediction is available
+            if hasattr(churn_model, "predict_proba"):
+                churn_risk = float(churn_model.predict_proba(features)[0][1])
+            else:
+                churn_risk = float(churn_model.predict(features)[0])
+            models_used.append(CHURN_MODEL)
+            
+        # Predict SLA Risk
+        sla_model = _load_model(SLA_MODEL)
+        if sla_model:
+            if hasattr(sla_model, "predict_proba"):
+                sla_breach_risk = float(sla_model.predict_proba(features)[0][1])
+            else:
+                sla_breach_risk = float(sla_model.predict(features)[0])
+            models_used.append(SLA_MODEL)
+            
+    except (ImportError, ModuleNotFoundError) as e:
+        log.debug("ml_modules_missing_using_heuristics", error=str(e))
+    except Exception as e:
+        log.warning("ml_risk_prediction_failed_using_heuristics", error=str(e))
+        
+    # 2. Fall back to high-quality heuristics if ML prediction failed or models not found
+    if sla_breach_risk is None or churn_risk is None:
+        h_sla, h_churn = calculate_heuristic_risks(body, category, priority, tier)
+        sla_breach_risk = sla_breach_risk if sla_breach_risk is not None else h_sla
+        churn_risk = churn_risk if churn_risk is not None else h_churn
+        models_used.append("heuristic-risk-engine-v1.0")
+        
+    log.info("churn_agent_done", sla_breach_risk=sla_breach_risk, churn_risk=churn_risk, models_used=models_used)
+    
+    return {
+        **state,
+        "sla_breach_risk": sla_breach_risk,
+        "churn_risk": churn_risk,
+        "current_step": "churn_agent",
+        "models_used": models_used,
+    }

src/agent/nodes/classify_agent.py

@@ -0,0 +1,143 @@
+import mlflow
+import os
+import re
+import structlog
+from typing import List
+from src.agent.state import AgentState
+
+log = structlog.get_logger()
+
+CATEGORY_MODEL = "customercore-category-classifier"
+PRIORITY_MODEL = "customercore-priority-classifier"
+
+CATEGORIES = [
+    "bug", "feature_request", "security", "performance",
+    "billing", "auth", "docs", "question", "incident", "other"
+]
+PRIORITIES = ["low", "medium", "high", "critical"]
+
+def _load_model(name: str):
+    """Attempt to load classifier model from MLflow tracking server."""
+    tracking_uri = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:///mlruns.db")
+    mlflow.set_tracking_uri(tracking_uri)
+    client = mlflow.MlflowClient()
+    try:
+        versions = client.get_latest_versions(name, stages=["Production", "None"])
+        if versions:
+            return mlflow.sklearn.load_model(f"models:/{name}/{versions[0].version}")
+    except Exception as e:
+        log.warning("model_load_failed", name=name, error=str(e))
+    return None
+
+def heuristic_classify(body: str) -> tuple[str, str]:
+    """
+    Fallback high-quality heuristic classification for categories and priorities.
+    Matches industry support ticket routing logic.
+    """
+    text = body.lower()
+    
+    # ── Category Heuristics ────────────────────────────────────────────────────
+    category = "other"
+    
+    if any(kw in text for kw in ["hacked", "leak", "unauthorized", "vulnerability", "breach", "cve", "compromised", "exploit", "security", "sicherheit", "fuite", "breche", "brecha", "exposed", "exposé", "expuesto", "divulgada", "divulgado"]):
+        category = "security"
+    elif any(kw in text for kw in ["outage", "offline", "completely down", "service unavailable", "is down", "not accessible", "500 error", "ausfall", "hors service", "panne", "caído", "caido"]):
+        category = "incident"
+    elif any(kw in text for kw in ["login", "password", "oauth", "token", "signin", "sign-in", "signup", "mfa", "2fa", "authentication", "einloggen", "passwort", "passworts", "mot de passe", "connexion", "contraseña", "iniciar sesión", "senha", "entrar", "konto gesperrt", "gesperrt"]):
+        category = "auth"
+    elif any(kw in text for kw in ["billing", "payment", "charge", "refund", "invoice", "stripe", "checkout", "cost", "pay", "rechnung", "zahlung", "facture", "paiement", "factura", "pago"]):
+        category = "billing"
+    elif any(kw in text for kw in ["slow", "latency", "lag", "timeout", "degraded", "delay", "performance", "high cpu"]):
+        category = "performance"
+    elif any(kw in text for kw in ["docs", "documentation", "how to", "guide", "tutorial", "where can i find"]):
+        category = "docs"
+    elif any(kw in text for kw in ["feature request", "would be nice", "can we add", "suggest", "improve", "request feature"]):
+        category = "feature_request"
+    elif any(kw in text for kw in ["bug", "error", "fail", "broken", "issue", "crash", "wrong", "incorrect", "exception"]):
+        category = "bug"
+    elif "?" in text:
+        category = "question"
+        
+    # ── Priority Heuristics ────────────────────────────────────────────────────
+    priority = "medium"
+    
+    # Critical criteria
+    if category in ["security", "incident"] or any(kw in text for kw in ["completely down", "production down", "outage", "all users affected"]):
+        priority = "critical"
+    # High criteria
+    elif any(kw in text for kw in ["urgent", "blocking", "failed", "broken", "asap", "invoice issue", "payment failed", "error", "dringend", "gesperrt", "fehlgeschlagen", "kaputt", "bloqué", "bloque", "urgente", "bloqueado", "bloqueada"]):
+        priority = "high"
+    # Low criteria
+    elif category in ["docs", "feature_request"] or any(kw in text for kw in ["minor", "typo", "cosmetic", "wont fix"]):
+        priority = "low"
+        
+    return category, priority
+
+def classify_agent_node(state: AgentState) -> AgentState:
+    ticket = state["ticket"]
+    body = ticket["body"]
+    
+    # 1. Try MLflow ML models first
+    category = None
+    priority = None
+    models_used: List[str] = state.get("models_used") or []
+    
+    try:
+        # Check if feature engineering module exists
+        from src.ml.feature_engineering import create_structured_features
+        import pandas as pd
+        
+        df = pd.DataFrame([{
+            "body": body,
+            "priority": "medium",
+            "customer_tier": ticket.get("customer_tier", "professional"),
+            "reopen_count": 0,
+            "ticket_age_hours": 24,
+        }])
+        features = create_structured_features(df)
+        
+        # Predict Category
+        cat_model = _load_model(CATEGORY_MODEL)
+        if cat_model:
+            cat_idx = cat_model.predict(features)[0]
+            category = CATEGORIES[int(cat_idx) % len(CATEGORIES)]
+            models_used.append(CATEGORY_MODEL)
+            
+        # Predict Priority
+        pri_model = _load_model(PRIORITY_MODEL)
+        if pri_model:
+            pri_idx = pri_model.predict(features)[0]
+            priority = PRIORITIES[int(pri_idx) % len(PRIORITIES)]
+            models_used.append(PRIORITY_MODEL)
+            
+    except (ImportError, ModuleNotFoundError) as e:
+        log.debug("ml_modules_missing_using_heuristics", error=str(e))
+    except Exception as e:
+        log.warning("ml_prediction_failed_using_heuristics", error=str(e))
+        
+    # 2. Fall back to high-quality heuristics if ML prediction failed or models not found
+    if not category or not priority:
+        h_cat, h_pri = heuristic_classify(body)
+        category = category or h_cat
+        priority = priority or h_pri
+        models_used.append("heuristic-classifier-v1.0")
+        
+    # 3. Apply customer tier multiplier: VIP customer gets upgraded priority
+    tier = ticket.get("customer_tier", "free").lower()
+    if tier == "enterprise":
+        if priority == "low":
+            priority = "medium"
+        elif priority == "medium":
+            priority = "high"
+        elif priority == "high":
+            priority = "critical"
+            
+    log.info("classify_agent_done", category=category, priority=priority, models_used=models_used)
+    
+    return {
+        **state,
+        "category": category,
+        "priority": priority,
+        "current_step": "classify_agent",
+        "models_used": models_used,
+    }

src/agent/nodes/hitl_agent.py

@@ -0,0 +1,95 @@
+import structlog
+from typing import List
+from src.agent.state import AgentState
+
+log = structlog.get_logger()
+
+def compute_confidence(state: AgentState) -> float:
+    """
+    Evaluate triage confidence based on category ambiguity, input lengths,
+    and models used during processing.
+    """
+    confidence = 0.90  # Default high-fidelity baseline
+    
+    ticket = state["ticket"]
+    body = ticket["body"]
+    category = state.get("category", "other")
+    models_used = state.get("models_used") or []
+    
+    # 1. Ticket length penalty (extremely short or long tickets add ambiguity)
+    body_len = len(body.strip())
+    if body_len < 10:
+        confidence -= 0.35  # extremely short, practically zero context
+    elif body_len < 30:
+        confidence -= 0.15
+    elif body_len > 2500:
+        confidence -= 0.05
+        
+    # 2. Category ambiguity penalty
+    if category == "other":
+        confidence -= 0.10
+        
+    # 3. Model pedigree penalty: using fallback heuristics rather than ML/LLM models
+    if "heuristic-classifier-v1.0" in models_used:
+        confidence -= 0.05
+    if "heuristic-rag-fallback-v1.0" in models_used:
+        confidence -= 0.10
+        
+    # 4. Risk premium check: elevated churn/SLA risks make the triage decision more sensitive
+    churn_risk = state.get("churn_risk") or 0.0
+    sla_risk = state.get("sla_breach_risk") or 0.0
+    if churn_risk > 0.70 or sla_risk > 0.75:
+        confidence -= 0.05
+        
+    return max(0.1, min(1.0, round(confidence, 2)))
+
+def hitl_agent_node(state: AgentState) -> AgentState:
+    priority = state.get("priority", "medium")
+    category = state.get("category", "other")
+    sla_breach_risk = state.get("sla_breach_risk") or 0.0
+    churn_risk = state.get("churn_risk") or 0.0
+    
+    models_used: List[str] = state.get("models_used") or []
+    models_used.append("hitl-threshold-evaluator-v1.0")
+    
+    # Calculate confidence score
+    confidence = compute_confidence(state)
+    
+    # Evaluate HITL (Human-in-the-loop) criteria
+    hitl_required = False
+    reasons = []
+    
+    if confidence < 0.65:
+        hitl_required = True
+        reasons.append(f"Low confidence ({confidence:.2f} < 0.65)")
+        
+    if sla_breach_risk > 0.80:
+        hitl_required = True
+        reasons.append(f"Critical SLA breach risk ({sla_breach_risk:.2f} > 0.80)")
+        
+    if churn_risk > 0.75 and priority in ["high", "critical"]:
+        hitl_required = True
+        reasons.append(f"High churn risk ({churn_risk:.2f} > 0.75) on high/critical priority ticket")
+        
+    if category == "security":
+        # Enterprise policy: ALWAYS review security incidents to ensure data privacy and legal compliance
+        hitl_required = True
+        reasons.append("Security compliance policy review required")
+        
+    hitl_reason = "; ".join(reasons) if hitl_required else None
+    
+    log.info(
+        "hitl_agent_done",
+        confidence=confidence,
+        hitl_required=hitl_required,
+        hitl_reason=hitl_reason
+    )
+    
+    return {
+        **state,
+        "confidence": confidence,
+        "hitl_required": hitl_required,
+        "hitl_reason": hitl_reason,
+        "current_step": "hitl_agent",
+        "models_used": models_used,
+    }

src/agent/nodes/incident_agent.py

@@ -0,0 +1,69 @@
+import structlog
+from typing import List
+from src.agent.state import AgentState
+
+log = structlog.get_logger()
+
+# Keywords indicating an operational infrastructure or software incident
+INCIDENT_KEYWORDS = [
+    "outage", "completely down", "service unavailable", "is down", "not accessible",
+    "500 error", "internal server error", "database connection failed", "server crash",
+    "network failure", "incident", "dns failure", "timeout error", "api failure"
+]
+
+def scan_incident(body: str) -> bool:
+    """Scan ticket body for operational incident indicator keywords."""
+    text = body.lower()
+    return any(kw in text for kw in INCIDENT_KEYWORDS)
+
+def incident_agent_node(state: AgentState) -> AgentState:
+    ticket = state["ticket"]
+    body = ticket["body"]
+    category = state.get("category", "other")
+    priority = state.get("priority", "medium")
+    tier = ticket.get("customer_tier", "free").lower()
+    
+    models_used: List[str] = state.get("models_used") or []
+    models_used.append("incident-routing-heuristics-v1.0")
+    
+    # 1. Detect if this is an active/severe incident
+    incident_detected = scan_incident(body) or (category == "incident")
+    
+    # 2. Determine routing team based on category, priority, and customer tier
+    routing_team = "support"  # default
+    
+    if tier == "enterprise" and priority == "critical":
+        # Enterprise VIP tickets requiring critical escalation route to dedicated escalation team
+        routing_team = "escalation"
+    elif category == "security":
+        routing_team = "security"
+    elif category == "billing":
+        routing_team = "billing"
+    elif category == "incident":
+        routing_team = "infra"
+    elif category == "performance":
+        routing_team = "infra"
+    elif category == "auth":
+        routing_team = "security"
+    elif category in ["bug", "feature_request"]:
+        routing_team = "engineering"
+    elif category in ["docs", "question", "other"]:
+        routing_team = "support"
+        
+    # If an incident was detected but the ticket is not already routed to escalation/security/billing,
+    # route to infra or engineering to address immediately
+    if incident_detected and routing_team not in ["escalation", "security", "billing"]:
+        if category in ["bug", "performance"]:
+            routing_team = "engineering"
+        else:
+            routing_team = "infra"
+            
+    log.info("incident_agent_done", incident_detected=incident_detected, routing_team=routing_team)
+    
+    return {
+        **state,
+        "incident_detected": incident_detected,
+        "routing_team": routing_team,
+        "current_step": "incident_agent",
+        "models_used": models_used,
+    }

src/agent/nodes/memory_agent.py

@@ -0,0 +1,21 @@
+from src.agent.state import AgentState
+from src.agent.memory import recall_memories
+import structlog
+
+log = structlog.get_logger()
+
+def memory_agent_node(state: AgentState) -> AgentState:
+    ticket = state["ticket"]
+    customer_id = ticket["customer_id"]
+    tenant_id = ticket["tenant_id"]
+    body = ticket["body"]
+
+    # Recall up to 3 relevant long-term memories
+    memories = recall_memories(customer_id, tenant_id, query=body, limit=3)
+
+    log.info("memory_agent_done", customer_id=customer_id, memories_recalled=len(memories))
+    return {
+        **state,
+        "recalled_memories": memories,
+        "current_step": "memory_agent",
+    }

src/agent/nodes/rag_agent.py

@@ -0,0 +1,198 @@
+import json
+import hashlib
+import os
+import structlog
+from typing import List
+from src.agent.state import AgentState
+from src.rag.hybrid_retriever import get_retriever
+from src.rag.graph_rag import GraphRAGEngine
+from src.rag.llm_client import get_client
+from src.rag.multilingual import detect_language, SUPPORTED_LANGUAGES
+
+log = structlog.get_logger()
+
+# Thread-safe in-memory cache for local fallback when Redis is absent
+_local_semantic_cache = {}
+
+def get_redis_client():
+    """Attempt to get redis client if REDIS_URL is configured."""
+    redis_url = os.environ.get("REDIS_URL", "")
+    if not redis_url:
+        return None
+    try:
+        import redis
+        return redis.from_url(redis_url, decode_responses=True)
+    except Exception as e:
+        log.warning("redis_connection_failed_using_local_fallback", error=str(e))
+        return None
+
+def lookup_cache(tenant_id: str, body: str) -> dict | None:
+    """Look up ticket resolution in Redis semantic cache or local fallback."""
+    key_hash = hashlib.sha256(f"{tenant_id}:{body.strip()}".encode("utf-8")).hexdigest()
+    cache_key = f"cc:semcache:{key_hash}"
+    
+    redis_client = get_redis_client()
+    if redis_client:
+        try:
+            cached_val = redis_client.get(cache_key)
+            if cached_val:
+                log.info("semantic_cache_hit_redis", tenant_id=tenant_id)
+                return json.loads(cached_val)
+        except Exception as e:
+            log.warning("redis_lookup_failed", error=str(e))
+            
+    # Fallback to local in-memory dict
+    if cache_key in _local_semantic_cache:
+        log.info("semantic_cache_hit_local", tenant_id=tenant_id)
+        return _local_semantic_cache[cache_key]
+        
+    return None
+
+def store_cache(tenant_id: str, body: str, data: dict):
+    """Store ticket resolution in Redis semantic cache or local fallback."""
+    key_hash = hashlib.sha256(f"{tenant_id}:{body.strip()}".encode("utf-8")).hexdigest()
+    cache_key = f"cc:semcache:{key_hash}"
+    
+    redis_client = get_redis_client()
+    if redis_client:
+        try:
+            redis_client.setex(cache_key, 3600 * 24, json.dumps(data)) # 24 hour TTL
+            log.info("semantic_cache_stored_redis", tenant_id=tenant_id)
+            return
+        except Exception as e:
+            log.warning("redis_store_failed", error=str(e))
+            
+    # Fallback to local in-memory dict
+    _local_semantic_cache[cache_key] = data
+    log.info("semantic_cache_stored_locally", tenant_id=tenant_id)
+
+def rag_agent_node(state: AgentState) -> AgentState:
+    ticket = state["ticket"]
+    body = ticket["body"]
+    tenant_id = ticket["tenant_id"]
+    
+    models_used: List[str] = state.get("models_used") or []
+    
+    # 1. Check Redis / Local Semantic Cache first
+    cached_result = lookup_cache(tenant_id, body)
+    if cached_result:
+        models_used.append("redis-semantic-cache-v1")
+        return {
+            **state,
+            "summary": cached_result["summary"],
+            "suggested_resolution": cached_result["suggested_resolution"],
+            "kb_citations": cached_result["kb_citations"],
+            "current_step": "rag_agent",
+            "models_used": models_used,
+        }
+        
+    # 2. Cache Miss: Run Hybrid Vector + Graph-RAG Retrieval
+    log.info("semantic_cache_miss_running_graph_rag", tenant_id=tenant_id)
+    retriever = get_retriever()
+    engine = GraphRAGEngine(retriever=retriever)
+    
+    # Run Graph-RAG query
+    rag_result = engine.query(
+        tenant_id=tenant_id,
+        question=body,
+        k=5,
+        include_sql=True
+    )
+    
+    # 3. Detect ticket language
+    lang = detect_language(body)
+    lang_display = SUPPORTED_LANGUAGES.get(lang, "English")
+    
+    # 4. Invoke cloud LLM via LiteLLM client wrapper
+    client = get_client()
+    
+    system_prompt = (
+        "You are an expert enterprise support triage agent. Your job is to analyze the support ticket "
+        "and generate a short summary, a detailed suggested resolution, and a list of knowledge base citation IDs.\n\n"
+        "You are provided with rich tenant context and similar past resolved tickets from our Graph-RAG engine.\n\n"
+        "=== CRITICAL INSTRUCTIONS ===\n"
+        f"1. The ticket is written in {lang_display}. You MUST output the 'summary' and 'suggested_resolution' in {lang_display}.\n"
+        "2. The summary must be a single concise sentence summarizing the core issue (max 300 characters).\n"
+        "3. The suggested resolution must be highly specific, technical, and actionable (max 1000 characters).\n"
+        "4. Output a list of knowledge base citation IDs. Valid IDs MUST start with 'KB-', 'TICKET-', or 'DOC-'. "
+        "Extract these from the provided similar past tickets or tenant profile where applicable. If none exist, output an empty list.\n"
+        "5. You MUST return your response as a strict JSON object with exactly three keys: 'summary', 'suggested_resolution', and 'kb_citations'. "
+        "Do not include any markdown framing, backticks, or text before/after the JSON."
+    )
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"Context:\n{rag_result.combined_context}\n\nTicket Body: {body}"}
+    ]
+    
+    try:
+        response = client.call_cloud(messages=messages, temperature=0.1)
+        if response.success:
+            models_used.append(response.model_used)
+            # Parse the JSON response
+            clean_content = response.content.strip()
+            if clean_content.startswith("```json"):
+                clean_content = clean_content.split("```json")[1].split("```")[0].strip()
+            elif clean_content.startswith("```"):
+                clean_content = clean_content.split("```")[1].split("```")[0].strip()
+                
+            parsed = json.loads(clean_content)
+            summary = parsed.get("summary", "")
+            suggested_resolution = parsed.get("suggested_resolution", "")
+            kb_citations = parsed.get("kb_citations", [])
+            
+            # Clean and validate citations
+            valid_citations = []
+            for citation in kb_citations:
+                citation_str = str(citation).upper().strip()
+                if citation_str.startswith(("KB-", "TICKET-", "DOC-")):
+                    valid_citations.append(citation_str)
+                else:
+                    # Fix formatting if possible
+                    if citation_str.isalnum():
+                        valid_citations.append(f"KB-{citation_str}")
+            
+            # Ensure they are non-empty and satisfy length constraints
+            if not summary:
+                summary = body[:100] + "..." if len(body) > 100 else body
+            if not suggested_resolution:
+                suggested_resolution = "Our team is investigating this issue. We will update you shortly."
+                
+            # Store in cache
+            result_data = {
+                "summary": summary,
+                "suggested_resolution": suggested_resolution,
+                "kb_citations": valid_citations,
+            }
+            store_cache(tenant_id, body, result_data)
+            
+            log.info("rag_agent_completed_successfully", model=response.model_used)
+            return {
+                **state,
+                "summary": summary,
+                "suggested_resolution": suggested_resolution,
+                "kb_citations": valid_citations,
+                "current_step": "rag_agent",
+                "models_used": models_used,
+            }
+            
+    except Exception as e:
+        log.error("rag_agent_llm_failed_using_fallback", error=str(e))
+        
+    # Heuristic fallback if LLM call or parsing fails
+    summary = body[:150] + "..." if len(body) > 150 else body
+    suggested_resolution = (
+        f"A support agent has been notified of this ticket. "
+        f"Language detected: {lang_display}. Category classified: {state.get('category', 'unknown')}."
+    )
+    kb_citations = ["KB-FALLBACK"]
+    models_used.append("heuristic-rag-fallback-v1.0")
+    
+    return {
+        **state,
+        "summary": summary,
+        "suggested_resolution": suggested_resolution,
+        "kb_citations": kb_citations,
+        "current_step": "rag_agent_fallback",
+        "models_used": models_used,
+    }

src/agent/schemas.py

@@ -0,0 +1,131 @@
+from pydantic import BaseModel, Field, field_validator
+from typing import Optional, List, Literal
+
+VALID_CATEGORIES = [
+    "bug", "feature_request", "security", "performance",
+    "billing", "auth", "docs", "question", "incident", "other",
+]
+
+VALID_PRIORITIES = ["critical", "high", "medium", "low"]
+VALID_TEAMS = ["support", "engineering", "infra", "billing", "security", "escalation"]
+
+class TriageOutput(BaseModel):
+    """14-field structured output for every ticket processed by the triage agent."""
+
+    # Field 1: Category classification
+    category: Literal[
+        "bug", "feature_request", "security", "performance",
+        "billing", "auth", "docs", "question", "incident", "other"
+    ] = Field(description="Ticket category determined by the classifier agent")
+
+    # Field 2: Priority classification
+    priority: Literal["critical", "high", "medium", "low"] = Field(
+        description="Ticket priority. critical=SLA breach risk. high=blocking. medium=normal. low=cosmetic"
+    )
+
+    # Field 3: Routing team
+    routing_team: Literal["support", "engineering", "infra", "billing", "security", "escalation"] = Field(
+        description="Team this ticket should be routed to"
+    )
+
+    # Field 4: SLA breach risk score
+    sla_breach_risk: float = Field(
+        ge=0.0, le=1.0,
+        description="Probability of SLA breach (0.0=safe, 1.0=certain breach)"
+    )
+
+    # Field 5: Churn risk score
+    churn_risk: float = Field(
+        ge=0.0, le=1.0,
+        description="Customer churn risk score based on ticket history and billing signals"
+    )
+
+    # Field 6: Confidence score
+    confidence: float = Field(
+        ge=0.0, le=1.0,
+        description="Agent confidence in this triage decision. < 0.65 triggers HITL"
+    )
+
+    # Field 7: Short summary of the ticket
+    summary: str = Field(
+        min_length=10, max_length=300,
+        description="One sentence summary of the ticket issue"
+    )
+
+    # Field 8: Suggested resolution
+    suggested_resolution: str = Field(
+        min_length=10, max_length=1000,
+        description="Recommended resolution or next action based on KB and ticket history"
+    )
+
+    # Field 9: Knowledge base citations
+    kb_citations: List[str] = Field(
+        default_factory=list,
+        description="List of KB article IDs that support this triage decision"
+    )
+
+    # Field 10: Customer memories recalled
+    recalled_memories: List[str] = Field(
+        default_factory=list,
+        description="Relevant memories recalled from previous interactions with this customer"
+    )
+
+    # Field 11: Incident detected
+    incident_detected: bool = Field(
+        description="True if this ticket is part of or related to a detected incident"
+    )
+
+    # Field 12: HITL required
+    hitl_required: bool = Field(
+        description="True if a human must review this triage before acting on it"
+    )
+
+    # Field 13: HITL reason
+    hitl_reason: Optional[str] = Field(
+        default=None,
+        description="Reason HITL is required, if hitl_required is True"
+    )
+
+    # Field 14: Model versions used
+    models_used: List[str] = Field(
+        default_factory=list,
+        description="List of model names used in this triage (for audit and debugging)"
+    )
+
+    @field_validator("hitl_reason")
+    @classmethod
+    def hitl_reason_required_when_hitl(cls, v, info):
+        if info.data.get("hitl_required") and not v:
+            raise ValueError("hitl_reason must be provided when hitl_required is True")
+        return v
+
+    @field_validator("kb_citations")
+    @classmethod
+    def validate_citations_format(cls, v):
+        for citation in v:
+            if not citation.startswith(("KB-", "TICKET-", "DOC-")):
+                raise ValueError(f"Invalid citation format: {citation}. Must start with KB-, TICKET-, or DOC-")
+        return v
+
+    def should_trigger_hitl(self) -> bool:
+        return self.confidence < 0.65 or self.hitl_required
+
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "category": "auth",
+                "priority": "high",
+                "routing_team": "security",
+                "sla_breach_risk": 0.72,
+                "churn_risk": 0.35,
+                "confidence": 0.88,
+                "summary": "Customer reports 401 errors after OAuth token refresh",
+                "suggested_resolution": "Verify token expiry settings and redirect URI configuration",
+                "kb_citations": ["KB-001"],
+                "recalled_memories": ["Previous auth issue resolved with token rotation - 2025-03-01"],
+                "incident_detected": False,
+                "hitl_required": False,
+                "hitl_reason": None,
+                "models_used": ["priority-classifier-v2", "category-classifier-v2", "gemma-3-4b"],
+            }
+        }

src/agent/state.py

@@ -0,0 +1,38 @@
+from typing import TypedDict, Optional, List, Annotated
+from langgraph.graph.message import add_messages
+from src.agent.schemas import TriageOutput
+
+class TicketInput(TypedDict):
+    ticket_id: str
+    body: str
+    customer_id: str
+    tenant_id: str
+    customer_tier: str
+
+class AgentState(TypedDict):
+    # Input
+    ticket: TicketInput
+
+    # Populated by each sub-agent
+    category: Optional[str]
+    priority: Optional[str]
+    routing_team: Optional[str]
+    sla_breach_risk: Optional[float]
+    churn_risk: Optional[float]
+    confidence: Optional[float]
+    summary: Optional[str]
+    suggested_resolution: Optional[str]
+    kb_citations: Optional[List[str]]
+    recalled_memories: Optional[List[str]]
+    incident_detected: Optional[bool]
+    hitl_required: Optional[bool]
+    hitl_reason: Optional[str]
+    models_used: Optional[List[str]]
+
+    # Workflow metadata
+    current_step: Optional[str]
+    error: Optional[str]
+    final_output: Optional[TriageOutput]
+
+    # Messages (for LangGraph message passing)
+    messages: Annotated[list, add_messages]

src/agent/supervisor.py

@@ -0,0 +1,199 @@
+import structlog
+from typing import Dict, Any, Optional
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import MemorySaver
+
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except ImportError:
+    pass
+
+from src.agent.state import AgentState
+from src.agent.schemas import TriageOutput
+from src.agent.nodes.classify_agent import classify_agent_node
+from src.agent.nodes.memory_agent import memory_agent_node
+from src.agent.nodes.rag_agent import rag_agent_node
+from src.agent.nodes.churn_agent import churn_agent_node
+from src.agent.nodes.incident_agent import incident_agent_node
+from src.agent.nodes.hitl_agent import hitl_agent_node
+
+log = structlog.get_logger()
+
+def hitl_interrupt_node(state: AgentState) -> AgentState:
+    """Break point identity node. Execution halts immediately before this node is run."""
+    log.info("hitl_interrupt_node_reached_pausing")
+    return state
+
+def finalize_node(state: AgentState) -> AgentState:
+    """Assembles all calculated parameters into a strict, validated TriageOutput."""
+    log.info("finalizing_agent_state_compiling_schema")
+    
+    # Default fallbacks to ensure compliance with field validators/minimum lengths
+    summary = state.get("summary") or "Ticket requiring triage."
+    if len(summary) < 10:
+        summary = (summary + " " * 10)[:15]
+        
+    suggested_resolution = state.get("suggested_resolution") or "Resolution pending."
+    if len(suggested_resolution) < 10:
+        suggested_resolution = (suggested_resolution + " " * 10)[:15]
+        
+    final_output = TriageOutput(
+        category=state.get("category") or "other",
+        priority=state.get("priority") or "medium",
+        routing_team=state.get("routing_team") or "support",
+        sla_breach_risk=state.get("sla_breach_risk") or 0.0,
+        churn_risk=state.get("churn_risk") or 0.0,
+        confidence=state.get("confidence") or 0.5,
+        summary=summary,
+        suggested_resolution=suggested_resolution,
+        kb_citations=state.get("kb_citations") or [],
+        recalled_memories=state.get("recalled_memories") or [],
+        incident_detected=state.get("incident_detected") or False,
+        hitl_required=state.get("hitl_required") or False,
+        hitl_reason=state.get("hitl_reason"),
+        models_used=state.get("models_used") or []
+    )
+    
+    return {
+        **state,
+        "final_output": final_output,
+        "current_step": "finalize"
+    }
+
+# ── Define the Agentic State Workflow ──────────────────────────────────────────
+workflow = StateGraph(AgentState)
+
+# 1. Register specialized sub-agent nodes
+workflow.add_node("classify", classify_agent_node)
+workflow.add_node("memory", memory_agent_node)
+workflow.add_node("rag", rag_agent_node)
+workflow.add_node("churn", churn_agent_node)
+workflow.add_node("incident", incident_agent_node)
+workflow.add_node("hitl", hitl_agent_node)
+workflow.add_node("hitl_interrupt", hitl_interrupt_node)
+workflow.add_node("finalize", finalize_node)
+
+# 2. Add sequential transitions
+workflow.add_edge(START, "classify")
+workflow.add_edge("classify", "memory")
+workflow.add_edge("memory", "rag")
+workflow.add_edge("rag", "churn")
+workflow.add_edge("churn", "incident")
+workflow.add_edge("incident", "hitl")
+
+# 3. Add conditional Human-in-the-loop gating
+def hitl_check_router(state: AgentState) -> str:
+    """Enforce human intercept routing if flagged by the HITL agent."""
+    if state.get("hitl_required"):
+        log.info("routing_to_human_interrupt")
+        return "hitl_interrupt"
+    log.info("routing_directly_to_finalize")
+    return "finalize"
+
+workflow.add_conditional_edges(
+    "hitl",
+    hitl_check_router,
+    {
+        "hitl_interrupt": "hitl_interrupt",
+        "finalize": "finalize"
+    }
+)
+
+# 4. Final transitions
+workflow.add_edge("hitl_interrupt", "finalize")
+workflow.add_edge("finalize", END)
+
+# ── Compile the Graph with Durable Memory Checkpointer ─────────────────────────
+memory_checkpointer = MemorySaver()
+app = workflow.compile(
+    checkpointer=memory_checkpointer,
+    interrupt_before=["hitl_interrupt"]
+)
+
+# ── Public Entry Points for the Triage Pipeline ────────────────────────────────
+
+def run_triage(ticket: dict, thread_id: str = "default-thread") -> TriageOutput:
+    """
+    Run the multi-agent triage pipeline.
+    
+    If the graph hits a Human-in-the-loop interruption, the state is paused,
+    and a preliminary TriageOutput is returned with hitl_required=True.
+    Use resume_triage() to proceed.
+    """
+    initial_state = {
+        "ticket": ticket,
+        "category": None,
+        "priority": None,
+        "routing_team": None,
+        "sla_breach_risk": None,
+        "churn_risk": None,
+        "confidence": None,
+        "summary": None,
+        "suggested_resolution": None,
+        "kb_citations": None,
+        "recalled_memories": None,
+        "incident_detected": None,
+        "hitl_required": None,
+        "hitl_reason": None,
+        "models_used": [],
+        "current_step": "start",
+        "error": None,
+        "final_output": None,
+        "messages": []
+    }
+    
+    config = {"configurable": {"thread_id": thread_id}}
+    
+    # Execute the graph
+    for event in app.stream(initial_state, config):
+        pass
+        
+    state_snapshot = app.get_state(config)
+    if state_snapshot.next:
+        # Paused before hitl_interrupt. Assemble from current intermediate parameters.
+        current_values = state_snapshot.values
+        
+        summary = current_values.get("summary") or "Ticket awaiting triage review."
+        if len(summary) < 10:
+            summary = (summary + " " * 10)[:15]
+            
+        suggested_resolution = current_values.get("suggested_resolution") or "Pending human triage approval."
+        if len(suggested_resolution) < 10:
+            suggested_resolution = (suggested_resolution + " " * 10)[:15]
+            
+        return TriageOutput(
+            category=current_values.get("category") or "other",
+            priority=current_values.get("priority") or "medium",
+            routing_team=current_values.get("routing_team") or "support",
+            sla_breach_risk=current_values.get("sla_breach_risk") or 0.0,
+            churn_risk=current_values.get("churn_risk") or 0.0,
+            confidence=current_values.get("confidence") or 0.5,
+            summary=summary,
+            suggested_resolution=suggested_resolution,
+            kb_citations=current_values.get("kb_citations") or [],
+            recalled_memories=current_values.get("recalled_memories") or [],
+            incident_detected=current_values.get("incident_detected") or False,
+            hitl_required=True,
+            hitl_reason=current_values.get("hitl_reason") or "Manual review required.",
+            models_used=current_values.get("models_used") or []
+        )
+        
+    return state_snapshot.values.get("final_output")
+
+def resume_triage(thread_id: str, overrides: Optional[Dict[str, Any]] = None) -> TriageOutput:
+    """
+    Resume an interrupted triage pipeline, optionally applying human corrections.
+    """
+    config = {"configurable": {"thread_id": thread_id}}
+    
+    if overrides:
+        # Human agent overrides the AI decisions
+        app.update_state(config, overrides)
+        
+    # Resume graph execution
+    for event in app.stream(None, config):
+        pass
+        
+    state_snapshot = app.get_state(config)
+    return state_snapshot.values.get("final_output")

src/dbt/.user.yml

@@ -0,0 +1 @@
+id: e384ffcc-f3ea-4709-bda5-25a26b0c069b

src/dbt/dbt_project.yml

@@ -0,0 +1,22 @@
+name: "customercore_dbt"
+version: "1.0.0"
+config-version: 2
+
+profile: "customercore_dbt"
+
+model-paths: ["models"]
+analysis-paths: ["analyses"]
+test-paths: ["tests"]
+seed-paths: ["seeds"]
+macro-paths: ["macros"]
+
+target-path: "target"
+clean-targets:
+  - "target"
+  - "dbt_packages"
+
+models:
+  customercore_dbt:
+    gold:
+      +materialized: table
+      +schema: gold

src/dbt/models/gold/billing_failure_summary.sql

@@ -0,0 +1,12 @@
+{{ config(materialized="table") }}
+
+SELECT
+    tenant_id,
+    DATE(created_at) AS event_date,
+    priority,
+    COUNT(*) AS billing_event_count,
+    SUM(CASE WHEN body LIKE '%payment_failed%' THEN 1 ELSE 0 END) AS payment_failures,
+    SUM(CASE WHEN body LIKE '%cancelled%' THEN 1 ELSE 0 END)      AS cancellations
+FROM {{ ref('stg_silver_events') }}
+WHERE event_type = 'billing_event'
+GROUP BY 1, 2, 3

src/dbt/models/gold/customer_health_daily.sql

@@ -0,0 +1,58 @@
+{{ config(
+    materialized="table",
+    unique_key="customer_id || '|' || tenant_id || '|' || snapshot_date"
+) }}
+
+WITH tickets AS (
+    SELECT
+        customer_id,
+        tenant_id,
+        COUNT(*) AS open_ticket_count,
+        AVG(CASE priority
+            WHEN 'critical' THEN 4
+            WHEN 'high'     THEN 3
+            WHEN 'medium'   THEN 2
+            ELSE 1 END) AS avg_priority_score,
+        SUM(CASE WHEN priority IN ('critical','high') THEN 1 ELSE 0 END) AS high_priority_count
+    FROM {{ ref('stg_silver_events') }}
+    WHERE event_type = 'ticket'
+      AND created_at >= CURRENT_DATE - INTERVAL '30' DAY
+    GROUP BY 1, 2
+),
+
+billing AS (
+    SELECT
+        customer_id,
+        tenant_id,
+        COUNT(*) FILTER (WHERE body LIKE '%payment_failed%') AS payment_failures_30d,
+        MAX(created_at) AS last_billing_event
+    FROM {{ ref('stg_silver_events') }}
+    WHERE event_type = 'billing_event'
+      AND created_at >= CURRENT_DATE - INTERVAL '30' DAY
+    GROUP BY 1, 2
+),
+
+product AS (
+    SELECT
+        customer_id,
+        tenant_id,
+        COUNT(*) AS product_events_30d
+    FROM {{ ref('stg_silver_events') }}
+    WHERE event_type = 'product_event'
+      AND created_at >= CURRENT_DATE - INTERVAL '30' DAY
+    GROUP BY 1, 2
+)
+
+SELECT
+    COALESCE(t.customer_id, b.customer_id, p.customer_id) AS customer_id,
+    COALESCE(t.tenant_id,   b.tenant_id,   p.tenant_id)   AS tenant_id,
+    CURRENT_DATE                                            AS snapshot_date,
+    COALESCE(t.open_ticket_count, 0)    AS open_tickets,
+    COALESCE(t.avg_priority_score, 1.0) AS avg_priority,
+    COALESCE(t.high_priority_count, 0)  AS high_priority_tickets,
+    COALESCE(b.payment_failures_30d, 0) AS payment_failures_30d,
+    b.last_billing_event,
+    COALESCE(p.product_events_30d, 0)   AS product_events_30d
+FROM tickets t
+FULL OUTER JOIN billing b USING (customer_id, tenant_id)
+FULL OUTER JOIN product  p USING (customer_id, tenant_id)

src/dbt/models/gold/incident_severity_hourly.sql

@@ -0,0 +1,11 @@
+{{ config(materialized="table") }}
+
+SELECT
+    tenant_id,
+    DATE_TRUNC('hour', CAST(created_at AS TIMESTAMP)) AS incident_hour,
+    priority AS severity,
+    COUNT(*)  AS incident_count,
+    COUNT(DISTINCT customer_id) AS affected_customers
+FROM {{ ref('stg_silver_events') }}
+WHERE event_type = 'incident'
+GROUP BY 1, 2, 3

src/dbt/models/gold/product_adoption_features.sql

@@ -0,0 +1,11 @@
+{{ config(materialized="table") }}
+
+SELECT
+    tenant_id,
+    customer_id,
+    DATE(created_at) AS event_date,
+    COUNT(*)         AS total_product_events,
+    COUNT(DISTINCT DATE(created_at)) AS active_days
+FROM {{ ref('stg_silver_events') }}
+WHERE event_type = 'product_event'
+GROUP BY 1, 2, 3

src/dbt/models/gold/retention_cohort_metrics.sql

@@ -0,0 +1,33 @@
+{{ config(materialized="table") }}
+
+WITH first_seen AS (
+    SELECT
+        customer_id,
+        tenant_id,
+        MIN(DATE(created_at)) AS cohort_date
+    FROM {{ ref('stg_silver_events') }}
+    WHERE customer_id IS NOT NULL
+    GROUP BY 1, 2
+),
+
+activity AS (
+    SELECT
+        customer_id,
+        tenant_id,
+        DATE(created_at) AS active_date
+    FROM {{ ref('stg_silver_events') }}
+    WHERE customer_id IS NOT NULL
+    GROUP BY 1, 2, 3
+)
+
+SELECT
+    f.cohort_date,
+    f.tenant_id,
+    COUNT(DISTINCT f.customer_id) AS cohort_size,
+    COUNT(DISTINCT CASE WHEN a.active_date <= f.cohort_date + INTERVAL '7' DAY
+        THEN a.customer_id END) AS retained_d7,
+    COUNT(DISTINCT CASE WHEN a.active_date <= f.cohort_date + INTERVAL '30' DAY
+        THEN a.customer_id END) AS retained_d30
+FROM first_seen f
+LEFT JOIN activity a USING (customer_id, tenant_id)
+GROUP BY 1, 2

src/dbt/models/gold/stg_silver_events.sql

@@ -0,0 +1,36 @@
+{{ config(materialized="view") }}
+
+SELECT
+    event_id,
+    CASE 
+        WHEN event_type = 'support_ticket_created' THEN 'ticket'
+        WHEN event_type = 'incident_detected' THEN 'incident'
+        WHEN event_type IN ('usability_complaint', 'feature_request', 'feature_praise') THEN 'product_event'
+        WHEN event_type IN ('payment_failed', 'payment_method_expired', 'subscription_downgrade', 'overcharge_reported', 'invoice_dispute') THEN 'billing_event'
+        ELSE event_type
+    END AS event_type,
+    source,
+    COALESCE(tenant_id, 'system') AS tenant_id,
+    customer_id,
+    COALESCE(original_timestamp, timestamp, processed_at)::timestamp AS created_at,
+    body,
+    priority,
+    COALESCE(reopen_count::integer, 0) AS reopen_count,
+    false AS is_synthetic,
+    
+    -- Billing Specific Fields
+    amount::double AS billing_amount,
+    currency AS billing_currency,
+    plan AS billing_plan,
+    failure_code AS billing_failure_code,
+    
+    -- Product Specific Fields
+    sentiment_score::double AS product_sentiment_score,
+    satisfaction_rating::integer AS product_satisfaction_rating,
+    
+    -- Incident Specific Fields
+    severity AS incident_severity,
+    affected_service AS incident_affected_service,
+    error_rate::double AS incident_error_rate
+    
+FROM {{ source('silver', 'silver_events') }}

src/dbt/models/gold/support_agent_performance.sql

@@ -0,0 +1,13 @@
+{{ config(materialized="table") }}
+
+SELECT
+    tenant_id,
+    source,
+    DATE(created_at)  AS report_date,
+    priority,
+    COUNT(*)          AS tickets_created,
+    COUNT(DISTINCT customer_id) AS unique_customers_served,
+    AVG(LENGTH(body)) AS avg_body_length
+FROM {{ ref('stg_silver_events') }}
+WHERE event_type = 'ticket'
+GROUP BY 1, 2, 3, 4

src/dbt/models/gold/ticket_funnel_daily.sql

@@ -0,0 +1,13 @@
+{{ config(materialized="table") }}
+
+SELECT
+    tenant_id,
+    event_type,
+    priority,
+    source,
+    DATE(created_at)       AS event_date,
+    COUNT(*)               AS event_count,
+    COUNT(DISTINCT customer_id) AS unique_customers
+FROM {{ ref('stg_silver_events') }}
+WHERE event_type = 'ticket'
+GROUP BY 1, 2, 3, 4, 5

src/dbt/models/schema.yml

@@ -0,0 +1,101 @@
+version: 2
+
+models:
+  - name: customer_health_daily
+    description: "Per-customer health metrics computed daily from all signal sources"
+    columns:
+      - name: customer_id
+        description: "Unique identifier for the customer"
+        tests:
+          - not_null
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: snapshot_date
+        description: "Date when metrics snapshot was taken"
+        tests:
+          - not_null
+      - name: avg_priority
+        description: "Weighted average priority score of tickets (1-4)"
+        tests:
+          - not_null
+          - dbt_utils.accepted_range:
+              min_value: 1
+              max_value: 4
+
+  - name: ticket_funnel_daily
+    description: "Daily ticket volume by category, priority, and source"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: event_count
+        description: "Total ticket count for the day"
+        tests:
+          - not_null
+
+  - name: incident_severity_hourly
+    description: "Hourly incident counts by severity"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: incident_count
+        description: "Number of system incidents detected"
+        tests:
+          - not_null
+
+  - name: billing_failure_summary
+    description: "Daily billing failures and subscription cancellations summary"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: billing_event_count
+        description: "Total billing events registered"
+        tests:
+          - not_null
+
+  - name: product_adoption_features
+    description: "Daily count of product interactions per customer account"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: customer_id
+        description: "Unique identifier for the customer"
+        tests:
+          - not_null
+      - name: total_product_events
+        description: "Total product usage events"
+        tests:
+          - not_null
+
+  - name: retention_cohort_metrics
+    description: "Weekly and monthly retention metrics by customer signup cohorts"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: cohort_size
+        description: "Number of new customers signed up on the cohort date"
+        tests:
+          - not_null
+
+  - name: support_agent_performance
+    description: "Daily summary of ticket volume and descriptive stats for agents"
+    columns:
+      - name: tenant_id
+        description: "B2B SaaS tenant identifier"
+        tests:
+          - not_null
+      - name: tickets_created
+        description: "Number of tickets processed for this slice"
+        tests:
+          - not_null

src/dbt/models/sources.yml

@@ -0,0 +1,10 @@
+version: 2
+
+sources:
+  - name: silver
+    description: "Silver layer S3 Parquet tables from Python streaming pipeline"
+    tables:
+      - name: silver_events
+        description: "All PII-masked, schema-validated events from all 4 sources"
+        meta:
+          external_location: "read_parquet('s3://customercore-lake/silver/*/*.parquet', union_by_name=True)"

src/dbt/package-lock.yml

@@ -0,0 +1,5 @@
+packages:
+  - name: dbt_utils
+    package: dbt-labs/dbt_utils
+    version: 1.3.0
+sha1_hash: 226ae69cdfbc9367e2aa2c472b01f99dbce11de0

src/dbt/packages.yml

@@ -0,0 +1,3 @@
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.3.0

src/dbt/profiles.yml

@@ -0,0 +1,16 @@
+customercore_dbt:
+  target: dev
+  outputs:
+    dev:
+      type: duckdb
+      path: "src/dbt/customercore.duckdb"
+      schema: gold
+      extensions:
+        - httpfs
+        - iceberg
+      settings:
+        s3_endpoint: "localhost:9000"
+        s3_access_key_id: "minioadmin"
+        s3_secret_access_key: "minioadmin"
+        s3_use_ssl: false
+        s3_url_style: "path"

src/rag/__init__.py

@@ -0,0 +1,14 @@
+"""
+src/rag/__init__.py
+
+CustomerCore RAG (Retrieval-Augmented Generation) package.
+
+Modules:
+  hybrid_retriever  — ChromaDB dense search + BM25 with tenant isolation
+  semantic_cache    — 3-layer cache (L0 embedding, L1 Redis exact, L2 vector)
+  reranker          — Cross-encoder reranking of merged retrieval results
+  tool_rag          — Dynamic semantic tool schema retrieval (Phase 8)
+  graph_rag         — Unified B2B graph-RAG: semantic + relational traversal (Phase 8)
+  router            — SLA-aware multi-model LLM routing (Phase 7)
+  llm_client        — LiteLLM wrapper routing to Ollama/OpenRouter
+"""

src/rag/graph_rag.py

@@ -0,0 +1,617 @@
+"""
+src/rag/graph_rag.py
+
+Phase 8b: Unified B2B Graph-RAG Engine
+
+== Why Graph-RAG Beats Pure Vector RAG for B2B ==
+
+Pure vector RAG answers "find me tickets similar to this one."
+That works for simple FAQ-style queries but breaks for B2B intelligence:
+
+  Q: "Why has acme-corp been escalating so often this quarter?"
+  Pure vector: Returns similar escalation tickets — no context.
+  Graph-RAG:   Returns similar tickets + tenant health score + category
+               breakdown from DuckDB Gold + escalation trend from time series.
+               The LLM gets BOTH the examples AND the structured analytics.
+
+B2B support platforms are fundamentally relational: tenants have accounts,
+accounts have tiers, tiers have SLAs, SLAs have breach patterns. Vector
+search alone misses all of this. Graph-RAG bridges the gap by combining:
+
+  Layer 1 (Graph)    — B2BKnowledgeGraph: NetworkX DiGraph connecting
+                        Tickets → Tenants → Categories → Metrics
+  Layer 2 (Vector)   — HybridRetriever: BM25 + ChromaDB dense search
+  Layer 3 (SQL)      — DuckDB queries against Gold mart Parquet files
+
+== Architecture ==
+
+    Query: "Why is acme-corp escalating this month?"
+      │
+      ├── [Vector]  HybridRetriever.search(tenant_id="acme-corp", query=...)
+      │    └── Returns: 5 most similar past escalation tickets
+      │
+      ├── [Graph]   B2BKnowledgeGraph.get_tenant_context("acme-corp")
+      │    └── Returns: total tickets, top categories, priority breakdown
+      │
+      └── [SQL]     DuckDB → Gold mart → support_agent_performance + ticket_funnel
+           └── Returns: volume trend, resolution rate, avg priority
+
+      ↓
+    GraphRAGEngine._format_combined_context(...)
+      └── Produces: structured prompt context for the LLM
+
+Run demo:
+  python -m src.rag.graph_rag
+"""
+
+import logging
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Optional, Any
+
+logger = logging.getLogger(__name__)
+
+# ── Node Dataclasses ───────────────────────────────────────────────────────────
+
+@dataclass
+class TicketNode:
+    ticket_id: str
+    tenant_id: str
+    text: str
+    category: str
+    priority: str
+    language: str = "en"
+    created_at: str = ""
+
+
+@dataclass
+class TenantNode:
+    tenant_id: str
+    tier: str = "professional"
+    metrics: dict = field(default_factory=dict)
+
+
+@dataclass
+class CategoryNode:
+    category: str
+    ticket_count: int = 0
+
+
+@dataclass
+class GraphRAGResult:
+    """Full result from a Graph-RAG query."""
+    query: str
+    tenant_id: str
+    similar_tickets: list        # list[RetrievedDoc]
+    tenant_context: dict
+    sql_insights: list[dict]
+    combined_context: str
+    query_plan: list[str]        # steps taken — for explainability/debugging
+
+
+# ── B2B Knowledge Graph ────────────────────────────────────────────────────────
+
+class B2BKnowledgeGraph:
+    """
+    In-memory knowledge graph for B2B customer intelligence.
+
+    Nodes:
+      - tenant:{tenant_id}    — B2B customer tenant
+      - ticket:{ticket_id}    — support ticket
+      - category:{name}       — support category (billing, technical, etc.)
+
+    Edges:
+      - (tenant) --[HAS_TICKET]--> (ticket)
+      - (ticket) --[BELONGS_TO]--> (category)
+      - (ticket) --[SIMILAR_TO]--> (ticket)   ← added by RAG engine after retrieval
+
+    Usage:
+        graph = B2BKnowledgeGraph()
+        graph.add_ticket("acme-corp", "TKT-001", "API error on checkout",
+                         category="technical", priority="high", language="en")
+        context = graph.get_tenant_context("acme-corp")
+    """
+
+    def __init__(self):
+        try:
+            import networkx as nx
+            self._graph = nx.DiGraph()
+        except ImportError:
+            logger.warning("networkx not installed — using dict-based fallback graph")
+            self._graph = None
+
+        # Fallback storage (always populated, networkx is additive)
+        self._tenants: dict[str, TenantNode] = {}
+        self._tickets: dict[str, TicketNode] = {}
+        self._categories: dict[str, CategoryNode] = {}
+        self._tenant_tickets: dict[str, list[str]] = defaultdict(list)
+        self._ticket_category: dict[str, str] = {}
+
+    def add_ticket(
+        self,
+        tenant_id: str,
+        ticket_id: str,
+        text: str,
+        category: str = "general",
+        priority: str = "medium",
+        language: str = "en",
+        created_at: str = "",
+    ):
+        """Add a ticket node and connect it to tenant and category nodes."""
+        # Create nodes
+        ticket = TicketNode(
+            ticket_id=ticket_id, tenant_id=tenant_id, text=text,
+            category=category, priority=priority, language=language,
+            created_at=created_at,
+        )
+        self._tickets[ticket_id] = ticket
+        self._ticket_category[ticket_id] = category
+
+        if tenant_id not in self._tenants:
+            self._tenants[tenant_id] = TenantNode(tenant_id=tenant_id)
+
+        if category not in self._categories:
+            self._categories[category] = CategoryNode(category=category)
+        self._categories[category].ticket_count += 1
+        self._tenant_tickets[tenant_id].append(ticket_id)
+
+        # NetworkX graph
+        if self._graph is not None:
+            self._graph.add_node(f"ticket:{ticket_id}", **ticket.__dict__)
+            self._graph.add_node(f"tenant:{tenant_id}")
+            self._graph.add_node(f"category:{category}")
+            self._graph.add_edge(f"tenant:{tenant_id}", f"ticket:{ticket_id}",
+                                 rel="HAS_TICKET")
+            self._graph.add_edge(f"ticket:{ticket_id}", f"category:{category}",
+                                 rel="BELONGS_TO", priority=priority)
+
+    def add_tenant_metrics(self, tenant_id: str, metrics: dict):
+        """Update a tenant node with structured metrics from the Gold layer."""
+        if tenant_id not in self._tenants:
+            self._tenants[tenant_id] = TenantNode(tenant_id=tenant_id)
+        self._tenants[tenant_id].metrics = metrics
+
+        if self._graph is not None:
+            self._graph.add_node(f"tenant:{tenant_id}", **metrics)
+
+    def get_tenant_context(self, tenant_id: str) -> dict:
+        """
+        Build a structured context dict for a tenant from the graph.
+        Includes ticket volume, category breakdown, priority distribution,
+        language breakdown, and any stored metrics.
+        """
+        ticket_ids = self._tenant_tickets.get(tenant_id, [])
+        tickets = [self._tickets[tid] for tid in ticket_ids if tid in self._tickets]
+
+        if not tickets:
+            return {
+                "tenant_id": tenant_id,
+                "total_tickets": 0,
+                "message": "No tickets found in graph for this tenant.",
+            }
+
+        # Category breakdown
+        category_counts: dict[str, int] = defaultdict(int)
+        priority_counts: dict[str, int] = defaultdict(int)
+        language_counts: dict[str, int] = defaultdict(int)
+
+        for t in tickets:
+            category_counts[t.category] += 1
+            priority_counts[t.priority] += 1
+            language_counts[t.language] += 1
+
+        top_categories = sorted(
+            category_counts.items(), key=lambda x: x[1], reverse=True
+        )[:5]
+
+        # Escalation proxy: high + critical tickets as % of total
+        escalation_count = (
+            priority_counts.get("high", 0) + priority_counts.get("critical", 0)
+        )
+        escalation_rate = round(escalation_count / len(tickets) * 100, 1)
+
+        tenant_node = self._tenants.get(tenant_id, TenantNode(tenant_id))
+
+        return {
+            "tenant_id": tenant_id,
+            "tier": tenant_node.tier,
+            "total_tickets": len(tickets),
+            "top_categories": [
+                {"category": cat, "count": cnt} for cat, cnt in top_categories
+            ],
+            "priority_breakdown": dict(priority_counts),
+            "language_breakdown": dict(language_counts),
+            "escalation_rate_pct": escalation_rate,
+            "escalation_count": escalation_count,
+            "gold_metrics": tenant_node.metrics,
+        }
+
+    def find_similar_tenants(
+        self, tenant_id: str, by: str = "category", top_k: int = 3
+    ) -> list[dict]:
+        """
+        Find tenants with similar issue profiles based on category distribution.
+        Useful for benchmarking: "how does acme-corp compare to similar tenants?"
+        """
+        target_cats = set(
+            self._tickets[tid].category
+            for tid in self._tenant_tickets.get(tenant_id, [])
+            if tid in self._tickets
+        )
+
+        scores = []
+        for other_id, ticket_ids in self._tenant_tickets.items():
+            if other_id == tenant_id:
+                continue
+            other_cats = set(
+                self._tickets[tid].category
+                for tid in ticket_ids if tid in self._tickets
+            )
+            # Jaccard similarity
+            if target_cats or other_cats:
+                overlap = len(target_cats & other_cats)
+                union = len(target_cats | other_cats)
+                similarity = overlap / union if union > 0 else 0.0
+                scores.append({"tenant_id": other_id, "similarity": round(similarity, 3)})
+
+        return sorted(scores, key=lambda x: x["similarity"], reverse=True)[:top_k]
+
+    def get_category_trends(self) -> dict[str, int]:
+        """Return ticket count per category across all tenants."""
+        return {cat: node.ticket_count for cat, node in self._categories.items()}
+
+    def node_count(self) -> int:
+        if self._graph is not None:
+            return self._graph.number_of_nodes()
+        return len(self._tickets) + len(self._tenants) + len(self._categories)
+
+    def edge_count(self) -> int:
+        if self._graph is not None:
+            return self._graph.number_of_edges()
+        return len(self._tickets) * 2  # each ticket has 2 edges
+
+
+# ── SQL Insights (DuckDB Gold Layer) ──────────────────────────────────────────
+
+def _load_duckdb_insights(tenant_id: str, gold_db_path: str = "data/gold/customercore_gold.duckdb") -> list[dict]:
+    """
+    Query the DuckDB Gold layer for structured analytics about a tenant.
+    Returns empty list gracefully if the DB doesn't exist (local dev mode).
+    """
+    insights = []
+    try:
+        import duckdb
+        conn = duckdb.connect(gold_db_path, read_only=True)
+
+        # Ticket funnel for this tenant
+        try:
+            rows = conn.execute("""
+                SELECT event_type, COUNT(*) as count, AVG(priority_score) as avg_priority
+                FROM gold_gold.ticket_funnel_daily
+                WHERE tenant_id = ?
+                GROUP BY event_type
+                ORDER BY count DESC
+                LIMIT 5
+            """, [tenant_id]).fetchall()
+            for r in rows:
+                insights.append({
+                    "source": "ticket_funnel",
+                    "event_type": r[0],
+                    "count": r[1],
+                    "avg_priority": round(r[2] or 0, 2),
+                })
+        except Exception:
+            pass
+
+        # Customer health
+        try:
+            rows = conn.execute("""
+                SELECT snapshot_date, avg_priority, ticket_count
+                FROM gold_gold.customer_health_daily
+                WHERE tenant_id = ?
+                ORDER BY snapshot_date DESC
+                LIMIT 3
+            """, [tenant_id]).fetchall()
+            for r in rows:
+                insights.append({
+                    "source": "customer_health",
+                    "date": str(r[0]),
+                    "avg_priority": round(float(r[1] or 0), 2),
+                    "ticket_count": r[2],
+                })
+        except Exception:
+            pass
+
+        conn.close()
+
+    except Exception as e:
+        logger.debug("DuckDB Gold layer not available: %s", e)
+
+    return insights
+
+
+# ── Graph-RAG Engine ───────────────────────────────────────────────────────────
+
+class GraphRAGEngine:
+    """
+    Unified B2B Graph-RAG Query Engine.
+
+    Combines three retrieval strategies in a single query:
+      1. Vector + BM25 hybrid search (tenant-isolated, Phase 6)
+      2. Graph traversal for tenant context and category trends
+      3. SQL analytics from DuckDB Gold marts
+
+    The resulting combined_context is ready for injection into any LLM prompt.
+
+    Usage:
+        engine = GraphRAGEngine(retriever=hybrid_retriever, graph=knowledge_graph)
+        result = engine.query(
+            tenant_id="acme-corp",
+            question="Why has this tenant been escalating so often this quarter?",
+            k=5
+        )
+        # Inject result.combined_context into your LLM prompt
+    """
+
+    def __init__(
+        self,
+        retriever=None,       # HybridRetriever instance (or None for graph-only)
+        graph: Optional[B2BKnowledgeGraph] = None,
+        gold_db_path: str = "data/gold/customercore_gold.duckdb",
+    ):
+        self.retriever = retriever
+        self.graph = graph or B2BKnowledgeGraph()
+        self.gold_db_path = gold_db_path
+
+    def index_ticket(
+        self,
+        tenant_id: str,
+        ticket_id: str,
+        text: str,
+        metadata: dict | None = None,
+    ):
+        """
+        Index a ticket into both the vector retriever and the knowledge graph.
+        Single entry point — no need to call retriever and graph separately.
+        """
+        metadata = metadata or {}
+        category = metadata.get("category", "general")
+        priority = metadata.get("priority", "medium")
+        language = metadata.get("language", "en")
+
+        # Add to graph
+        self.graph.add_ticket(
+            tenant_id=tenant_id,
+            ticket_id=ticket_id,
+            text=text,
+            category=category,
+            priority=priority,
+            language=language,
+        )
+
+        # Add to vector retriever if available
+        if self.retriever is not None:
+            self.retriever.index_ticket(tenant_id, ticket_id, text, metadata)
+
+    def query(
+        self,
+        tenant_id: str,
+        question: str,
+        k: int = 5,
+        include_sql: bool = True,
+    ) -> GraphRAGResult:
+        """
+        Full Graph-RAG query.
+
+        Steps (all recorded in query_plan for explainability):
+          1. Vector+BM25 hybrid retrieval (tenant-isolated)
+          2. Graph traversal for tenant context
+          3. SQL analytics from DuckDB Gold (optional)
+          4. Format combined context for LLM injection
+        """
+        query_plan = []
+
+        # Step 1: Hybrid vector retrieval
+        similar_tickets = []
+        if self.retriever is not None:
+            similar_tickets = self.retriever.search(tenant_id, question, k=k)
+            query_plan.append(
+                f"[Vector+BM25] Retrieved {len(similar_tickets)} similar tickets "
+                f"for tenant={tenant_id}"
+            )
+        else:
+            query_plan.append("[Vector+BM25] Retriever not configured — skipped")
+
+        # Step 2: Graph context
+        tenant_context = self.graph.get_tenant_context(tenant_id)
+        query_plan.append(
+            f"[Graph] Tenant context: {tenant_context['total_tickets']} tickets, "
+            f"escalation_rate={tenant_context.get('escalation_rate_pct', 0)}%"
+        )
+
+        # Step 3: SQL analytics
+        sql_insights = []
+        if include_sql:
+            sql_insights = _load_duckdb_insights(tenant_id, self.gold_db_path)
+            query_plan.append(
+                f"[SQL] Gold layer: {len(sql_insights)} insight rows from DuckDB"
+            )
+        else:
+            query_plan.append("[SQL] Skipped (include_sql=False)")
+
+        # Step 4: Format combined context
+        combined_context = self._format_combined_context(
+            question, similar_tickets, tenant_context, sql_insights
+        )
+        query_plan.append("[Format] Combined context built — ready for LLM injection")
+
+        return GraphRAGResult(
+            query=question,
+            tenant_id=tenant_id,
+            similar_tickets=similar_tickets,
+            tenant_context=tenant_context,
+            sql_insights=sql_insights,
+            combined_context=combined_context,
+            query_plan=query_plan,
+        )
+
+    def _format_combined_context(
+        self,
+        question: str,
+        similar_tickets: list,
+        tenant_context: dict,
+        sql_insights: list[dict],
+    ) -> str:
+        """
+        Format all retrieved information into a structured context string
+        ready for injection into an LLM system prompt.
+        """
+        lines = []
+
+        # Tenant profile
+        lines.append("=== TENANT PROFILE ===")
+        lines.append(f"Tenant ID : {tenant_context.get('tenant_id', 'unknown')}")
+        lines.append(f"Tier      : {tenant_context.get('tier', 'unknown')}")
+        lines.append(f"Total Tickets : {tenant_context.get('total_tickets', 0)}")
+        lines.append(f"Escalation Rate : {tenant_context.get('escalation_rate_pct', 0)}%")
+
+        top_cats = tenant_context.get("top_categories", [])
+        if top_cats:
+            cats_str = ", ".join(
+                f"{c['category']}({c['count']})" for c in top_cats[:3]
+            )
+            lines.append(f"Top Categories : {cats_str}")
+
+        lang_breakdown = tenant_context.get("language_breakdown", {})
+        if lang_breakdown:
+            lang_str = ", ".join(f"{k}:{v}" for k, v in lang_breakdown.items())
+            lines.append(f"Languages Used : {lang_str}")
+
+        # Similar past tickets
+        if similar_tickets:
+            lines.append("\n=== SIMILAR PAST TICKETS ===")
+            for i, doc in enumerate(similar_tickets[:5], 1):
+                lines.append(
+                    f"{i}. [{doc.category.upper()}|{doc.priority}] "
+                    f"{doc.text[:100]}..."
+                )
+
+        # SQL insights
+        if sql_insights:
+            lines.append("\n=== ANALYTICS (Gold Layer) ===")
+            for ins in sql_insights[:5]:
+                src = ins.get("source", "")
+                if src == "ticket_funnel":
+                    lines.append(
+                        f"- Funnel: {ins.get('event_type')} "
+                        f"count={ins.get('count')} "
+                        f"avg_priority={ins.get('avg_priority')}"
+                    )
+                elif src == "customer_health":
+                    lines.append(
+                        f"- Health [{ins.get('date')}]: "
+                        f"tickets={ins.get('ticket_count')} "
+                        f"avg_priority={ins.get('avg_priority')}"
+                    )
+
+        lines.append(f"\n=== ORIGINAL QUESTION ===\n{question}")
+        return "\n".join(lines)
+
+
+# ── Module-level singleton ─────────────────────────────────────────────────────
+_default_engine: Optional[GraphRAGEngine] = None
+
+
+def get_engine() -> GraphRAGEngine:
+    global _default_engine
+    if _default_engine is None:
+        _default_engine = GraphRAGEngine()
+    return _default_engine
+
+
+# ── Standalone Demo ────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    import os
+    os.environ["PYTHONIOENCODING"] = "utf-8"
+
+    print("=" * 65)
+    print("CustomerCore Phase 8b - Unified B2B Graph-RAG Demo")
+    print("=" * 65)
+
+    # Build knowledge graph
+    graph = B2BKnowledgeGraph()
+
+    # Index tickets for two tenants
+    acme_tickets = [
+        ("TKT-A001", "API returning 500 errors on checkout endpoint", "technical", "critical", "en"),
+        ("TKT-A002", "March invoice shows double charge for $420", "billing", "high", "en"),
+        ("TKT-A003", "Login broken after your latest deployment", "technical", "high", "en"),
+        ("TKT-A004", "Need to cancel our subscription next month", "subscription", "medium", "en"),
+        ("TKT-A005", "Export of customer data timing out after 30s", "technical", "high", "en"),
+        ("TKT-A006", "Zahlung fehlgeschlagen - bitte helfen", "billing", "high", "de"),
+        ("TKT-A007", "Notre tableau de bord ne fonctionne plus", "technical", "critical", "fr"),
+    ]
+    globex_tickets = [
+        ("TKT-B001", "Latency spike on EU cluster affecting production", "technical", "critical", "en"),
+        ("TKT-B002", "Overcharged by 2400 USD last billing cycle", "billing", "high", "en"),
+        ("TKT-B003", "Data export failing with timeout error", "technical", "high", "en"),
+    ]
+
+    print("\nIndexing tickets into knowledge graph...")
+    for tid, text, cat, prio, lang in acme_tickets:
+        graph.add_ticket("acme-corp", tid, text, cat, prio, lang)
+    for tid, text, cat, prio, lang in globex_tickets:
+        graph.add_ticket("globex-inc", tid, text, cat, prio, lang)
+
+    # Add mock tenant metrics
+    graph.add_tenant_metrics("acme-corp", {
+        "avg_resolution_hours": 4.2,
+        "open_tickets": 7,
+        "csat_score": 3.8,
+    })
+
+    print(f"Graph nodes: {graph.node_count()}")
+    print(f"Graph edges: {graph.edge_count()}")
+
+    # Tenant context
+    print("\n" + "=" * 40)
+    print("TENANT CONTEXT: acme-corp")
+    ctx = graph.get_tenant_context("acme-corp")
+    print(f"  Total Tickets    : {ctx['total_tickets']}")
+    print(f"  Escalation Rate  : {ctx['escalation_rate_pct']}%")
+    print(f"  Priority Breakdown: {ctx['priority_breakdown']}")
+    print(f"  Language Breakdown: {ctx['language_breakdown']}")
+    print(f"  Top Categories   : {[c['category'] for c in ctx['top_categories']]}")
+
+    # Similar tenants
+    print("\n" + "=" * 40)
+    print("SIMILAR TENANTS TO acme-corp:")
+    similar = graph.find_similar_tenants("acme-corp")
+    for s in similar:
+        print(f"  {s['tenant_id']} (similarity={s['similarity']})")
+
+    # Category trends
+    print("\n" + "=" * 40)
+    print("GLOBAL CATEGORY TRENDS:")
+    trends = graph.get_category_trends()
+    for cat, count in sorted(trends.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {cat:<15}: {count} tickets")
+
+    # Full Graph-RAG query (no retriever, no DuckDB in demo)
+    engine = GraphRAGEngine(retriever=None, graph=graph)
+    result = engine.query(
+        tenant_id="acme-corp",
+        question="Why has acme-corp been escalating so many technical issues this month?",
+        include_sql=False,  # no DuckDB in demo
+    )
+
+    print("\n" + "=" * 40)
+    print("GRAPH-RAG QUERY RESULT:")
+    print("\nQuery Plan:")
+    for step in result.query_plan:
+        print(f"  - {step}")
+
+    print("\nCombined Context (injected into LLM prompt):")
+    print("-" * 50)
+    print(result.combined_context)
+    print("=" * 65)

src/rag/hybrid_retriever.py

@@ -0,0 +1,470 @@
+"""
+src/rag/hybrid_retriever.py
+
+Phase 6: Multi-Tenant Vector DB Security + Hybrid Retrieval Pipeline
+
+== The Gap We Are Closing ==
+A naive ChromaDB implementation stores all tenant embeddings in one shared
+collection. If Tenant A searches for "billing refund" and Tenant B has
+similar tickets, Tenant B's private customer data can surface in Tenant A's
+results. For a B2B SaaS platform this is a catastrophic GDPR violation.
+
+== What This Does ==
+  1. STRICT TENANT ISOLATION — Every query to ChromaDB passes a metadata filter:
+       where={"tenant_id": current_tenant_id}
+     No results can ever come from a different tenant regardless of similarity score.
+
+  2. HYBRID RETRIEVAL — Two retrieval strategies run in parallel:
+     a. Dense semantic search via ChromaDB (embedding similarity)
+     b. Sparse keyword search via BM25 (exact/rare term matching)
+     Results are merged using Reciprocal Rank Fusion (RRF) — a proven
+     technique that usually outperforms either strategy alone.
+
+  3. CROSS-ENCODER RERANKING — Top-k merged results are re-scored by a
+     lightweight cross-encoder model to surface the most semantically
+     relevant results for the exact query.
+
+== Architecture Decision: Single Collection + Metadata Filter ==
+  We use ONE ChromaDB collection "customercore_tickets" for all tenants.
+  Isolation is enforced at query time via the `where` filter.
+  This is the standard enterprise pattern (used by Weaviate, Pinecone, Qdrant)
+  because it avoids managing hundreds of collections while keeping isolation.
+
+Run standalone demo:
+  python -m src.rag.hybrid_retriever
+"""
+
+import hashlib
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# ── Config ─────────────────────────────────────────────────────────────────────
+CHROMA_COLLECTION = "customercore_tickets"
+DEFAULT_DENSE_K = 10      # retrieve top-10 from ChromaDB
+DEFAULT_SPARSE_K = 10     # retrieve top-10 from BM25
+DEFAULT_FINAL_K = 5       # return top-5 after reranking
+RRF_CONSTANT = 60         # standard Reciprocal Rank Fusion constant
+
+
+@dataclass
+class RetrievedDoc:
+    """A single retrieved document with provenance metadata."""
+    doc_id: str
+    text: str
+    tenant_id: str
+    ticket_id: str = ""
+    category: str = ""
+    priority: str = ""
+    score: float = 0.0
+    source: str = ""        # "dense" | "sparse" | "fused"
+
+
+# ── Reciprocal Rank Fusion ─────────────────────────────────────────────────────
+
+def reciprocal_rank_fusion(
+    dense_results: list[RetrievedDoc],
+    sparse_results: list[RetrievedDoc],
+    k: int = RRF_CONSTANT,
+) -> list[RetrievedDoc]:
+    """
+    Merge dense and sparse result lists using Reciprocal Rank Fusion.
+    RRF score = Σ 1/(k + rank_i) for each list the document appears in.
+    Higher is better. Naturally handles result deduplication.
+    """
+    rrf_scores: dict[str, float] = {}
+    doc_map: dict[str, RetrievedDoc] = {}
+
+    for rank, doc in enumerate(dense_results, start=1):
+        rrf_scores[doc.doc_id] = rrf_scores.get(doc.doc_id, 0) + 1 / (k + rank)
+        doc_map[doc.doc_id] = doc
+
+    for rank, doc in enumerate(sparse_results, start=1):
+        rrf_scores[doc.doc_id] = rrf_scores.get(doc.doc_id, 0) + 1 / (k + rank)
+        if doc.doc_id not in doc_map:
+            doc_map[doc.doc_id] = doc
+
+    fused = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
+    results = []
+    for doc_id, score in fused:
+        doc = doc_map[doc_id]
+        doc.score = score
+        doc.source = "fused"
+        results.append(doc)
+    return results
+
+
+# ── BM25 Index (tenant-isolated) ───────────────────────────────────────────────
+
+class TenantBM25Index:
+    """
+    Lightweight in-memory BM25 index partitioned per tenant.
+    Uses rank_bm25.BM25Okapi under the hood.
+
+    In production this would be backed by Elasticsearch or OpenSearch
+    with a `tenant_id` field filter.
+    """
+
+    def __init__(self):
+        self._corpora: dict[str, list[str]] = {}      # tenant_id → [text, ...]
+        self._doc_ids: dict[str, list[str]] = {}       # tenant_id → [doc_id, ...]
+        self._doc_meta: dict[str, dict] = {}           # doc_id → metadata
+        self._indexes: dict[str, object] = {}          # tenant_id → BM25Okapi
+        self._dirty: set[str] = set()                  # tenants needing re-index
+
+    def add_document(self, tenant_id: str, doc_id: str, text: str, metadata: dict):
+        """Add a document to the tenant-isolated BM25 index."""
+        if tenant_id not in self._corpora:
+            self._corpora[tenant_id] = []
+            self._doc_ids[tenant_id] = []
+
+        # Avoid duplicates
+        if doc_id in self._doc_ids[tenant_id]:
+            return
+
+        self._corpora[tenant_id].append(text)
+        self._doc_ids[tenant_id].append(doc_id)
+        self._doc_meta[doc_id] = {**metadata, "tenant_id": tenant_id}
+        self._dirty.add(tenant_id)
+
+    def _build_index(self, tenant_id: str):
+        """Build/rebuild BM25Okapi index for a specific tenant."""
+        try:
+            from rank_bm25 import BM25Okapi
+        except ImportError:
+            raise ImportError(
+                "rank_bm25 is required for BM25 retrieval. "
+                "Install with: pip install rank-bm25"
+            )
+        tokenized = [doc.lower().split() for doc in self._corpora[tenant_id]]
+        self._indexes[tenant_id] = BM25Okapi(tokenized)
+        self._dirty.discard(tenant_id)
+
+    def search(self, tenant_id: str, query: str, k: int = 10) -> list[RetrievedDoc]:
+        """
+        Search the BM25 index for a specific tenant.
+        IMPORTANT: Only documents belonging to `tenant_id` are returned.
+        Cross-tenant results are architecturally impossible in this design.
+        """
+        if tenant_id not in self._corpora or not self._corpora[tenant_id]:
+            return []
+
+        if tenant_id in self._dirty:
+            self._build_index(tenant_id)
+
+        query_tokens = query.lower().split()
+        index = self._indexes[tenant_id]
+        scores = index.get_scores(query_tokens)
+
+        # Get top-k indices
+        top_k_idx = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
+
+        results = []
+        for idx in top_k_idx:
+            doc_id = self._doc_ids[tenant_id][idx]
+            meta = self._doc_meta.get(doc_id, {})
+            results.append(RetrievedDoc(
+                doc_id=doc_id,
+                text=self._corpora[tenant_id][idx],
+                tenant_id=tenant_id,
+                ticket_id=meta.get("ticket_id", ""),
+                category=meta.get("category", ""),
+                priority=meta.get("priority", ""),
+                score=float(scores[idx]),
+                source="sparse",
+            ))
+        return results
+
+    def document_count(self, tenant_id: Optional[str] = None) -> int:
+        if tenant_id:
+            return len(self._corpora.get(tenant_id, []))
+        return sum(len(v) for v in self._corpora.values())
+
+
+# ── ChromaDB Dense Retriever (tenant-isolated) ─────────────────────────────────
+
+class TenantChromaRetriever:
+    """
+    ChromaDB-backed dense semantic retriever with strict tenant isolation.
+
+    All tickets share a single collection. Tenant isolation is enforced
+    at query time using ChromaDB's `where` metadata filter.
+    This is the same pattern used by enterprise vector DBs (Weaviate, Pinecone).
+    """
+
+    def __init__(self, chroma_client=None, embedding_fn=None):
+        self._client = chroma_client
+        self._embedding_fn = embedding_fn
+        self._collection = None
+
+    def _get_collection(self):
+        if self._collection is None and self._client is not None:
+            self._collection = self._client.get_or_create_collection(
+                name=CHROMA_COLLECTION,
+                metadata={"hnsw:space": "cosine"},
+            )
+        return self._collection
+
+    def add_document(self, tenant_id: str, doc_id: str, text: str, metadata: dict):
+        """
+        Add a document to ChromaDB with tenant_id injected into metadata.
+        This is what guarantees query-time isolation.
+        """
+        collection = self._get_collection()
+        if collection is None:
+            return  # ChromaDB not configured — skip gracefully
+
+        embedding = self._embedding_fn([text])[0] if self._embedding_fn else None
+
+        collection.upsert(
+            ids=[doc_id],
+            documents=[text],
+            embeddings=[embedding] if embedding else None,
+            metadatas=[{
+                "tenant_id": tenant_id,   # ← CRITICAL: must be present for isolation
+                "ticket_id": metadata.get("ticket_id", ""),
+                "category": metadata.get("category", ""),
+                "priority": metadata.get("priority", ""),
+            }],
+        )
+
+    def search(self, tenant_id: str, query: str, k: int = 10) -> list[RetrievedDoc]:
+        """
+        Search ChromaDB with a mandatory tenant_id filter.
+
+        The `where={"tenant_id": tenant_id}` filter is applied at the
+        ChromaDB query engine level — not in Python after the fact.
+        This means ChromaDB will never even scan other tenants' embeddings.
+        """
+        collection = self._get_collection()
+        if collection is None:
+            return []
+
+        query_embedding = self._embedding_fn([query])[0] if self._embedding_fn else None
+
+        try:
+            results = collection.query(
+                query_texts=[query] if query_embedding is None else None,
+                query_embeddings=[query_embedding] if query_embedding is not None else None,
+                n_results=min(k, collection.count()),
+                where={"tenant_id": tenant_id},   # ← TENANT ISOLATION ENFORCED HERE
+                include=["documents", "metadatas", "distances"],
+            )
+        except Exception as e:
+            logger.warning("ChromaDB query failed: %s", e)
+            return []
+
+        docs = []
+        for i, (doc_text, meta, dist) in enumerate(zip(
+            results["documents"][0],
+            results["metadatas"][0],
+            results["distances"][0],
+        )):
+            # Cosine distance → similarity score (1 = identical, 0 = orthogonal)
+            score = 1.0 - dist
+            docs.append(RetrievedDoc(
+                doc_id=results["ids"][0][i],
+                text=doc_text,
+                tenant_id=meta.get("tenant_id", tenant_id),
+                ticket_id=meta.get("ticket_id", ""),
+                category=meta.get("category", ""),
+                priority=meta.get("priority", ""),
+                score=score,
+                source="dense",
+            ))
+        return docs
+
+
+# ── Hybrid Retriever ───────────────────────────────────────────────────────────
+
+class HybridRetriever:
+    """
+    Tenant-isolated Hybrid Retrieval Engine.
+
+    Combines ChromaDB dense search with BM25 sparse search using
+    Reciprocal Rank Fusion, with optional cross-encoder reranking.
+
+    Usage:
+        retriever = HybridRetriever()
+        retriever.index_ticket(tenant_id="acme-corp", ticket_id="TKT-001",
+                               text="API returning 500 errors on checkout",
+                               metadata={"category": "technical", "priority": "high"})
+
+        results = retriever.search(
+            tenant_id="acme-corp",
+            query="checkout page broken 500 error",
+            k=5
+        )
+    """
+
+    def __init__(
+        self,
+        chroma_client=None,
+        embedding_fn=None,
+        use_reranker: bool = False,
+    ):
+        self.dense = TenantChromaRetriever(chroma_client, embedding_fn)
+        self.sparse = TenantBM25Index()
+        self.use_reranker = use_reranker
+        self._reranker = None
+
+    def _get_reranker(self):
+        """Lazy-load cross-encoder reranker on first use."""
+        if self._reranker is None:
+            try:
+                from sentence_transformers import CrossEncoder
+                self._reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
+            except ImportError:
+                logger.warning("sentence-transformers not installed, reranking disabled")
+        return self._reranker
+
+    def index_ticket(
+        self,
+        tenant_id: str,
+        ticket_id: str,
+        text: str,
+        metadata: dict | None = None,
+    ):
+        """
+        Index a single support ticket into both dense and sparse indexes.
+        tenant_id is injected into ALL metadata — this is non-negotiable.
+        """
+        metadata = metadata or {}
+        doc_id = hashlib.sha256(f"{tenant_id}:{ticket_id}".encode()).hexdigest()[:16]
+        full_meta = {**metadata, "ticket_id": ticket_id}
+
+        self.dense.add_document(tenant_id, doc_id, text, full_meta)
+        self.sparse.add_document(tenant_id, doc_id, text, full_meta)
+
+    def search(
+        self,
+        tenant_id: str,
+        query: str,
+        k: int = DEFAULT_FINAL_K,
+        dense_k: int = DEFAULT_DENSE_K,
+        sparse_k: int = DEFAULT_SPARSE_K,
+    ) -> list[RetrievedDoc]:
+        """
+        Perform tenant-isolated hybrid retrieval + optional reranking.
+
+        1. Dense search in ChromaDB (filtered to tenant_id)
+        2. Sparse BM25 search (scoped to tenant partition)
+        3. Reciprocal Rank Fusion to merge lists
+        4. Optional cross-encoder reranking on top-k
+        5. Return final top-k results
+        """
+        t0 = time.time()
+
+        # Step 1 + 2: Parallel retrieval (both tenant-isolated)
+        dense_results = self.dense.search(tenant_id, query, k=dense_k)
+        sparse_results = self.sparse.search(tenant_id, query, k=sparse_k)
+
+        logger.debug(
+            "Retrieved: dense=%d sparse=%d for tenant=%s",
+            len(dense_results), len(sparse_results), tenant_id
+        )
+
+        # Step 3: Fuse
+        fused = reciprocal_rank_fusion(dense_results, sparse_results)[:k * 2]
+
+        # Step 4: Optional cross-encoder reranking
+        if self.use_reranker and fused:
+            reranker = self._get_reranker()
+            if reranker:
+                pairs = [[query, doc.text] for doc in fused]
+                scores = reranker.predict(pairs)
+                for doc, score in zip(fused, scores):
+                    doc.score = float(score)
+                    doc.source = "reranked"
+                fused = sorted(fused, key=lambda d: d.score, reverse=True)
+
+        elapsed = time.time() - t0
+        logger.debug("Hybrid retrieval completed in %.3fs", elapsed)
+
+        return fused[:k]
+
+    def doc_count(self, tenant_id: Optional[str] = None) -> int:
+        """Return number of indexed documents (BM25 count as proxy)."""
+        return self.sparse.document_count(tenant_id)
+
+
+# ── Module-level singleton ─────────────────────────────────────────────────────
+_default_retriever: Optional[HybridRetriever] = None
+
+
+def get_retriever() -> HybridRetriever:
+    """Return the module-level singleton HybridRetriever."""
+    global _default_retriever
+    if _default_retriever is None:
+        _default_retriever = HybridRetriever()
+    return _default_retriever
+
+
+# ── Standalone Demo ────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.WARNING)
+
+    print("=" * 60)
+    print("CustomerCore Phase 6 — Hybrid Retriever Demo")
+    print("Multi-Tenant Vector DB Isolation Test")
+    print("=" * 60)
+
+    retriever = HybridRetriever()
+
+    # Index some tickets for two tenants
+    tenant_a_tickets = [
+        ("TKT-A001", "Our checkout API is returning 500 errors on every request", {"category": "technical", "priority": "critical"}),
+        ("TKT-A002", "Billing invoice shows wrong amount for March", {"category": "billing", "priority": "high"}),
+        ("TKT-A003", "Login page is broken after your latest deploy", {"category": "technical", "priority": "high"}),
+        ("TKT-A004", "Need to update our payment method on file", {"category": "billing", "priority": "medium"}),
+        ("TKT-A005", "Our team cannot access the admin dashboard", {"category": "account", "priority": "high"}),
+    ]
+    tenant_b_tickets = [
+        ("TKT-B001", "CONFIDENTIAL: API latency spike on our EU cluster", {"category": "technical", "priority": "critical"}),
+        ("TKT-B002", "CONFIDENTIAL: We were overcharged by $2400 last month", {"category": "billing", "priority": "high"}),
+        ("TKT-B003", "CONFIDENTIAL: Data export is failing with timeout", {"category": "technical", "priority": "high"}),
+    ]
+
+    print("\nIndexing 5 tickets for Tenant A (acme-corp)...")
+    for ticket_id, text, meta in tenant_a_tickets:
+        retriever.index_ticket("acme-corp", ticket_id, text, meta)
+
+    print("Indexing 3 CONFIDENTIAL tickets for Tenant B (globex-inc)...")
+    for ticket_id, text, meta in tenant_b_tickets:
+        retriever.index_ticket("globex-inc", ticket_id, text, meta)
+
+    print(f"\nTotal indexed: {retriever.doc_count():,} docs")
+    print(f"  acme-corp:  {retriever.doc_count('acme-corp')} docs")
+    print(f"  globex-inc: {retriever.doc_count('globex-inc')} docs")
+
+    # Test 1: Tenant A searches — should only see Tenant A's tickets
+    print("\n" + "─" * 60)
+    print("TEST 1: Tenant A searches for 'billing issue API error'")
+    print("EXPECTED: Only acme-corp results. CONFIDENTIAL globex data must NOT appear.")
+    results_a = retriever.search("acme-corp", "billing issue API error", k=5)
+    all_correct = True
+    for doc in results_a:
+        icon = "✓" if doc.tenant_id == "acme-corp" else "✗ LEAK!"
+        print(f"  {icon} [{doc.tenant_id}] {doc.ticket_id}: {doc.text[:60]}...")
+        if doc.tenant_id != "acme-corp":
+            all_correct = False
+
+    print(f"\n  ISOLATION: {'PASSED ✓ — No cross-tenant leakage' if all_correct else 'FAILED ✗ — SECURITY BREACH!'}")
+
+    # Test 2: Tenant B searches — should only see Tenant B's tickets
+    print("\n" + "─" * 60)
+    print("TEST 2: Tenant B searches for 'API latency billing charge'")
+    print("EXPECTED: Only globex-inc CONFIDENTIAL results.")
+    results_b = retriever.search("globex-inc", "API latency billing charge", k=5)
+    all_correct_b = True
+    for doc in results_b:
+        icon = "✓" if doc.tenant_id == "globex-inc" else "✗ LEAK!"
+        print(f"  {icon} [{doc.tenant_id}] {doc.ticket_id}: {doc.text[:60]}...")
+        if doc.tenant_id != "globex-inc":
+            all_correct_b = False
+
+    print(f"\n  ISOLATION: {'PASSED ✓ — No cross-tenant leakage' if all_correct_b else 'FAILED ✗ — SECURITY BREACH!'}")
+    print("\n" + "=" * 60)

src/rag/llm_client.py

@@ -0,0 +1,263 @@
+"""
+src/rag/llm_client.py
+
+Unified LiteLLM client wrapper for CustomerCore.
+
+Provides a single interface for calling both:
+  - LOCAL models via Ollama (gemma3:4b, gemma2:2b) — $0, sub-200ms on GPU
+  - CLOUD models via OpenRouter (Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro)
+
+== Why LiteLLM? ==
+LiteLLM gives us a unified OpenAI-compatible interface for 100+ model providers.
+We can swap Ollama for vLLM, or swap OpenRouter for direct Anthropic, without
+changing any calling code — only the model string and API key change.
+
+== Production Config ==
+All API keys and endpoints are injected via Doppler at runtime.
+NEVER hardcode keys. The client reads from environment variables only.
+
+Environment variables:
+  OPENROUTER_API_KEY  — for cloud model routing
+  OLLAMA_BASE_URL     — defaults to http://localhost:11434
+  LLM_DEFAULT_LOCAL   — which local model to use (default: ollama/gemma3:4b)
+  LLM_DEFAULT_CLOUD   — which cloud model to use (default: openrouter/anthropic/claude-3.5-sonnet)
+"""
+
+import os
+import time
+import logging
+from dataclasses import dataclass, field
+from typing import Optional, Any
+
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except ImportError:
+    pass
+
+logger = logging.getLogger(__name__)
+
+# ── Model Identifiers ──────────────────────────────────────────────────────────
+# LiteLLM uses provider/model_name format
+LOCAL_MODEL = os.environ.get("LLM_DEFAULT_LOCAL", "ollama/gemma3:4b")
+CLOUD_MODEL = os.environ.get(
+    "LLM_DEFAULT_CLOUD",
+    # Verified working free OpenRouter models (benchmarked 2026-05-22):
+    #   openrouter/meta-llama/llama-3.1-8b-instruct  — 1.1s, excellent quality
+    #   openrouter/qwen/qwen3-8b                     — 5.3s, great reasoning
+    "openrouter/meta-llama/llama-3.1-8b-instruct"
+)
+OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
+OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "")
+
+
+@dataclass
+class LLMResponse:
+    """Structured response from any LLM call."""
+    content: str
+    model_used: str
+    provider: str                 # "local" | "cloud"
+    latency_ms: float
+    input_tokens: int = 0
+    output_tokens: int = 0
+    estimated_cost_usd: float = 0.0
+    success: bool = True
+    error: Optional[str] = None
+    raw: Optional[Any] = None
+
+
+# ── Cost table (approximate, USD per 1k tokens) ────────────────────────────────
+# Source: OpenRouter pricing — free models marked $0.000
+_COST_PER_1K_INPUT = {
+    "ollama/gemma3:4b": 0.0,
+    "ollama/gemma2:2b": 0.0,
+    "ollama/gemma4:e4b": 0.0,
+    # OpenRouter FREE models (free tier — no cost)
+    "openrouter/meta-llama/llama-3.1-8b-instruct:free": 0.0,
+    "openrouter/google/gemma-2-9b-it:free": 0.0,
+    "openrouter/mistralai/mistral-7b-instruct:free": 0.0,
+    "openrouter/microsoft/phi-3-mini-128k-instruct:free": 0.0,
+    # OpenRouter PAID models (for when budget allows)
+    "openrouter/anthropic/claude-3.5-sonnet": 0.003,
+    "openrouter/openai/gpt-4o": 0.005,
+    "openrouter/google/gemini-1.5-pro": 0.00125,
+}
+_COST_PER_1K_OUTPUT = {
+    "ollama/gemma3:4b": 0.0,
+    "ollama/gemma2:2b": 0.0,
+    "ollama/gemma4:e4b": 0.0,
+    # OpenRouter FREE models
+    "openrouter/meta-llama/llama-3.1-8b-instruct:free": 0.0,
+    "openrouter/google/gemma-2-9b-it:free": 0.0,
+    "openrouter/mistralai/mistral-7b-instruct:free": 0.0,
+    "openrouter/microsoft/phi-3-mini-128k-instruct:free": 0.0,
+    # OpenRouter PAID models
+    "openrouter/anthropic/claude-3.5-sonnet": 0.015,
+    "openrouter/openai/gpt-4o": 0.015,
+    "openrouter/google/gemini-1.5-pro": 0.005,
+}
+
+
+def _estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
+    cost_in = _COST_PER_1K_INPUT.get(model, 0.0) * input_tokens / 1000
+    cost_out = _COST_PER_1K_OUTPUT.get(model, 0.0) * output_tokens / 1000
+    return round(cost_in + cost_out, 6)
+
+
+class LLMClient:
+    """
+    Unified LiteLLM wrapper for local and cloud model calls.
+
+    Usage:
+        client = LLMClient()
+
+        # Local (free, fast, private)
+        response = client.call_local(
+            messages=[{"role": "user", "content": "Classify this ticket: API 500 error"}],
+            temperature=0.1,
+        )
+
+        # Cloud (frontier reasoning, higher cost)
+        response = client.call_cloud(
+            messages=[{"role": "user", "content": "Should we issue this $200 refund? Reason carefully."}],
+            temperature=0.2,
+        )
+    """
+
+    def __init__(
+        self,
+        local_model: str = LOCAL_MODEL,
+        cloud_model: str = CLOUD_MODEL,
+    ):
+        self.local_model = local_model
+        self.cloud_model = cloud_model
+
+    def _call(
+        self,
+        model: str,
+        messages: list[dict],
+        provider: str,
+        temperature: float = 0.1,
+        max_tokens: int = 512,
+        timeout: float = 30.0,
+    ) -> LLMResponse:
+        """Internal call — handles timing, error wrapping, cost estimation."""
+        try:
+            import litellm
+        except ImportError:
+            return LLMResponse(
+                content="",
+                model_used=model,
+                provider=provider,
+                latency_ms=0.0,
+                success=False,
+                error="litellm not installed",
+            )
+
+        # Configure Ollama base URL for local calls
+        api_base = OLLAMA_BASE_URL if provider == "local" else None
+        api_key = OPENROUTER_API_KEY if provider == "cloud" else None
+
+        t0 = time.perf_counter()
+        try:
+            kwargs = dict(
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                timeout=timeout,
+            )
+            if api_base:
+                kwargs["api_base"] = api_base
+            if api_key:
+                kwargs["api_key"] = api_key
+
+            # Add OpenRouter required headers
+            if provider == "cloud":
+                kwargs["extra_headers"] = {
+                    "HTTP-Referer": "https://github.com/CustomerCore",
+                    "X-Title": "CustomerCore B2B Intelligence Platform",
+                }
+
+            response = litellm.completion(**kwargs)
+            latency_ms = (time.perf_counter() - t0) * 1000
+
+            content = response.choices[0].message.content or ""
+            usage = response.usage or {}
+            input_tokens = getattr(usage, "prompt_tokens", 0) or 0
+            output_tokens = getattr(usage, "completion_tokens", 0) or 0
+
+            return LLMResponse(
+                content=content,
+                model_used=model,
+                provider=provider,
+                latency_ms=round(latency_ms, 1),
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                estimated_cost_usd=_estimate_cost(model, input_tokens, output_tokens),
+                success=True,
+                raw=response,
+            )
+
+        except Exception as e:
+            latency_ms = (time.perf_counter() - t0) * 1000
+            logger.error("LLM call failed: model=%s error=%s", model, e)
+            return LLMResponse(
+                content="",
+                model_used=model,
+                provider=provider,
+                latency_ms=round(latency_ms, 1),
+                success=False,
+                error=str(e),
+            )
+
+    def call_local(
+        self,
+        messages: list[dict],
+        temperature: float = 0.1,
+        max_tokens: int = 512,
+        timeout: float = 30.0,
+    ) -> LLMResponse:
+        """Call the local Ollama model. Zero cost. Data never leaves the machine."""
+        return self._call(
+            model=self.local_model,
+            messages=messages,
+            provider="local",
+            temperature=temperature,
+            max_tokens=max_tokens,
+            timeout=timeout,
+        )
+
+    def call_cloud(
+        self,
+        messages: list[dict],
+        temperature: float = 0.2,
+        max_tokens: int = 1024,
+        timeout: float = 60.0,
+    ) -> LLMResponse:
+        """Call the cloud frontier model via OpenRouter. Higher cost, higher capability."""
+        if not OPENROUTER_API_KEY:
+            logger.warning(
+                "OPENROUTER_API_KEY not set — falling back to local model for cloud call"
+            )
+            return self.call_local(messages, temperature, max_tokens, timeout)
+
+        return self._call(
+            model=self.cloud_model,
+            messages=messages,
+            provider="cloud",
+            temperature=temperature,
+            max_tokens=max_tokens,
+            timeout=timeout,
+        )
+
+
+# ── Module-level singleton ─────────────────────────────────────────────────────
+_default_client: Optional[LLMClient] = None
+
+
+def get_client() -> LLMClient:
+    global _default_client
+    if _default_client is None:
+        _default_client = LLMClient()
+    return _default_client

src/rag/multilingual.py

@@ -0,0 +1,208 @@
+"""
+src/rag/multilingual.py
+
+Phase 8a: Multi-Language Intelligence Layer
+
+== What This Does ==
+Every ticket entering CustomerCore now gets:
+  1. Language detection (langdetect — probabilistic, fast, 55 languages)
+  2. Language-aware routing (multilingual-capable models for non-English)
+  3. Language metadata stored on every Silver record for dbt Gold analysis
+  4. Multilingual BM25 tokenization (language-specific stopwords via NLTK)
+
+== Supported Languages (V1) ==
+  en — English (primary — Bitext SaaS + Banking datasets)
+  de — German  (important: you are in Germany, B2B EU platform)
+  fr — French  (major EU business language)
+  es — Spanish (3rd largest language by speakers)
+  pt — Portuguese (Brazil = large B2B market)
+  nl — Dutch
+  it — Italian
+  Unknown → fallback to multilingual model
+
+== Why Language Detection Matters for a B2B Platform ==
+Real enterprise B2B SaaS platforms serve global customers. A German Mittelstand
+company will write support tickets in German. A French SaaS company will
+write in French. Routing all tickets through an English-only model:
+  (a) Degrades classification accuracy by 15-40% for non-English tickets
+  (b) Misses language as a segmentation signal in analytics
+  (c) Is not acceptable for an EU-compliance platform (GDPR requires same
+      quality of service regardless of language used)
+
+== Dataset Sourcing ==
+  mteb/amazon_massive_intent — 11,514 rows per language, Apache 2.0
+  Languages: en, de, fr, es (confirmed working, no scraping, open license)
+  These are customer intent utterances — directly maps to support ticket classification.
+"""
+
+import logging
+import re
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# ── Supported language codes ───────────────────────────────────────────────────
+SUPPORTED_LANGUAGES = {
+    "en": "English",
+    "de": "German",
+    "fr": "French",
+    "es": "Spanish",
+    "pt": "Portuguese",
+    "nl": "Dutch",
+    "it": "Italian",
+}
+
+# ── Languages that need multilingual model routing ─────────────────────────────
+# English-only models (gemma3:4b) perform fine on English.
+# For other languages we prefer multilingual models.
+MULTILINGUAL_LANGUAGES = {"de", "fr", "es", "pt", "nl", "it"}
+
+# ── Simple stopword sets per language (for BM25 tokenization) ─────────────────
+# Minimal sets — good enough for BM25 without NLTK dependency
+_STOPWORDS: dict[str, set[str]] = {
+    "en": {"the", "a", "an", "is", "it", "in", "on", "at", "to", "for",
+           "of", "and", "or", "but", "my", "i", "we", "you", "our", "your"},
+    "de": {"der", "die", "das", "ein", "eine", "ist", "ich", "wir", "sie",
+           "und", "oder", "aber", "für", "mit", "von", "zu", "an", "auf"},
+    "fr": {"le", "la", "les", "un", "une", "est", "je", "nous", "vous",
+           "et", "ou", "mais", "pour", "avec", "de", "du", "au", "en"},
+    "es": {"el", "la", "los", "un", "una", "es", "yo", "nosotros", "ustedes",
+           "y", "o", "pero", "para", "con", "de", "del", "al", "en"},
+    "pt": {"o", "a", "os", "as", "um", "uma", "é", "eu", "nós", "você",
+           "e", "ou", "mas", "para", "com", "de", "do", "ao", "em"},
+    "nl": {"de", "het", "een", "is", "ik", "wij", "u", "en", "of", "maar",
+           "voor", "met", "van", "aan", "op", "in", "te"},
+    "it": {"il", "la", "i", "le", "un", "una", "è", "io", "noi", "voi",
+           "e", "o", "ma", "per", "con", "di", "del", "al", "in"},
+}
+
+
+def detect_language(text: str, min_text_length: int = 20) -> str:
+    """
+    Detect the language of a text string.
+
+    Returns an ISO 639-1 language code (e.g. 'en', 'de', 'fr').
+    Falls back to 'en' if:
+      - Text is too short for reliable detection
+      - langdetect is not installed
+      - Detection fails (mixed language, gibberish, etc.)
+
+    Uses langdetect under the hood (probabilistic Naive Bayes).
+    """
+    text = (text or "").strip()
+    if len(text) < min_text_length:
+        return "en"  # too short for reliable detection
+
+    try:
+        from langdetect import detect, LangDetectException
+        lang = detect(text)
+        # langdetect returns e.g. "zh-cn" — normalize to first part
+        lang = lang.split("-")[0].lower()
+        return lang
+    except ImportError:
+        logger.debug("langdetect not installed — returning 'en' as fallback")
+        return "en"
+    except Exception:
+        return "en"
+
+
+def detect_language_with_confidence(text: str) -> tuple[str, float]:
+    """
+    Detect language with confidence score.
+    Returns (language_code, probability 0.0-1.0).
+    """
+    text = (text or "").strip()
+    if len(text) < 20:
+        return "en", 1.0
+
+    try:
+        from langdetect import detect_langs, LangDetectException
+        results = detect_langs(text)
+        if results:
+            top = results[0]
+            lang = str(top.lang).split("-")[0].lower()
+            prob = float(top.prob)
+            return lang, round(prob, 3)
+        return "en", 1.0
+    except ImportError:
+        return "en", 1.0
+    except Exception:
+        return "en", 0.5
+
+
+def get_stopwords(lang: str) -> set[str]:
+    """Return stopword set for a language. Falls back to English."""
+    return _STOPWORDS.get(lang, _STOPWORDS["en"])
+
+
+def tokenize_multilingual(text: str, lang: str) -> list[str]:
+    """
+    Language-aware tokenization for BM25.
+    Lowercases, removes punctuation, filters stopwords.
+    """
+    # Simple unicode-friendly tokenization
+    tokens = re.findall(r"\b\w+\b", text.lower(), re.UNICODE)
+    stopwords = get_stopwords(lang)
+    return [t for t in tokens if t not in stopwords and len(t) > 1]
+
+
+def needs_multilingual_model(lang: str) -> bool:
+    """Return True if this language needs a multilingual model (not English-only)."""
+    return lang in MULTILINGUAL_LANGUAGES
+
+
+def get_language_display(lang: str) -> str:
+    """Return human-readable language name."""
+    return SUPPORTED_LANGUAGES.get(lang, f"Unknown ({lang})")
+
+
+def enrich_with_language(record: dict) -> dict:
+    """
+    Add language detection fields to a Silver record.
+    Mutates the record in place and returns it.
+
+    Added fields:
+      detected_language     — ISO 639-1 code (e.g. 'de')
+      language_confidence   — 0.0-1.0 detection confidence
+      language_display      — human readable (e.g. 'German')
+      is_multilingual       — True if non-English
+    """
+    text = record.get("body", "") or record.get("subject", "") or ""
+    lang, confidence = detect_language_with_confidence(text)
+
+    record["detected_language"] = lang
+    record["language_confidence"] = confidence
+    record["language_display"] = get_language_display(lang)
+    record["is_multilingual"] = needs_multilingual_model(lang)
+    return record
+
+
+# ── Standalone demo ────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    test_cases = [
+        ("en", "My payment failed and I cannot access my account. Please help urgently."),
+        ("de", "Meine Zahlung ist fehlgeschlagen und ich kann nicht auf mein Konto zugreifen."),
+        ("fr", "Mon paiement a échoué et je ne peux pas accéder à mon compte. Aidez-moi."),
+        ("es", "Mi pago falló y no puedo acceder a mi cuenta. Por favor ayúdame urgentemente."),
+        ("pt", "Meu pagamento falhou e não consigo acessar minha conta. Por favor, ajude."),
+        ("nl", "Mijn betaling is mislukt en ik kan geen toegang krijgen tot mijn account."),
+        ("it", "Il mio pagamento è fallito e non riesco ad accedere al mio account. Aiutami."),
+    ]
+
+    print("=" * 65)
+    print("CustomerCore Phase 8a — Language Detection Demo")
+    print("=" * 65)
+    print(f"\n{'Expected':<8} {'Detected':<8} {'Conf':<7} {'Multilingual':<14} Text")
+    print("─" * 70)
+
+    all_correct = True
+    for expected, text in test_cases:
+        lang, conf = detect_language_with_confidence(text)
+        is_ml = needs_multilingual_model(lang)
+        ok = "✓" if lang == expected else "✗"
+        if lang != expected:
+            all_correct = False
+        print(f"{ok} {expected:<6} {lang:<8} {conf:<7.2f} {str(is_ml):<14} {text[:40]}...")
+
+    print(f"\n{'All detections correct ✓' if all_correct else 'Some detections wrong ✗'}")
+    print("=" * 65)

src/rag/router.py

@@ -0,0 +1,377 @@
+"""
+src/rag/router.py
+
+Phase 7: SLA-Aware Multi-Model LLM Router
+
+== The Gap We Are Closing ==
+The previous setup had a manual local/cloud toggle — someone had to explicitly choose
+which model to call. Real enterprise B2B AI platforms (Sierra AI, Decagon) route
+automatically based on task complexity, SLA tier, and cost budget.
+
+== Routing Logic ==
+Every ticket that enters the system has a priority (low, medium, high, critical)
+and a task type (classification, extraction, reasoning, action):
+
+  Task Type     Priority      → Route To
+  ────────────  ────────────  ─────────────────────────────────────────────────
+  classify      low/medium    → LOCAL  (gemma3:4b via Ollama, ~150ms, $0)
+  classify      high/critical → LOCAL  (still fast enough, no sensitive action)
+  extract       low/medium    → LOCAL  (structured info extraction, deterministic)
+  extract       high/critical → LOCAL  (same — no judgment call required)
+  reason        low/medium    → LOCAL  (reasoning on routine tickets)
+  reason        high/critical → CLOUD  (frontier model for complex edge cases)
+  action        any           → CLOUD  (anything that takes an external action,
+                                        e.g. issue refund, create Jira ticket,
+                                        escalate to VIP team — requires best reasoning)
+
+== Why Not Always Use Cloud? ==
+  - 80%+ of B2B support tickets are routine (password resets, invoice requests)
+  - Local Gemma 3 4B handles these in ~150ms at $0 cost
+  - Cloud call costs ~$0.01-0.05 per ticket at scale = thousands per month
+  - SLA: low-priority tickets don't need frontier-model reasoning
+  - Privacy: local model means sensitive customer data never leaves the network
+
+== Metrics ==
+The router tracks every call: latency, model, cost, task type, priority.
+These feed into Prometheus metrics for the Grafana dashboard.
+
+Run standalone demo:
+  python -m src.rag.router
+"""
+
+import logging
+import os
+import time
+from dataclasses import dataclass, field
+from typing import Literal, Optional
+from collections import defaultdict
+
+from src.rag.llm_client import LLMClient, LLMResponse, get_client
+
+logger = logging.getLogger(__name__)
+
+# ── Types ──────────────────────────────────────────────────────────────────────
+Priority = Literal["low", "medium", "high", "critical"]
+TaskType = Literal["classify", "extract", "reason", "action"]
+ModelTier = Literal["local", "cloud"]
+
+
+# ── Routing Table ──────────────────────────────────────────────────────────────
+# (task_type, priority) → model tier
+ROUTING_TABLE: dict[tuple[str, str], ModelTier] = {
+    # Classification — always local (fast, cheap, sufficient)
+    ("classify", "low"):      "local",
+    ("classify", "medium"):   "local",
+    ("classify", "high"):     "local",
+    ("classify", "critical"): "local",
+
+    # Extraction — always local (deterministic structured output)
+    ("extract", "low"):      "local",
+    ("extract", "medium"):   "local",
+    ("extract", "high"):     "local",
+    ("extract", "critical"): "local",
+
+    # Reasoning — local for routine, cloud for high-stakes
+    ("reason", "low"):      "local",
+    ("reason", "medium"):   "local",
+    ("reason", "high"):     "cloud",   # ← switch to frontier model
+    ("reason", "critical"): "cloud",   # ← frontier model required
+
+    # Action — always cloud (external side effects require best judgment)
+    ("action", "low"):      "cloud",
+    ("action", "medium"):   "cloud",
+    ("action", "high"):     "cloud",
+    ("action", "critical"): "cloud",
+}
+
+# SLA latency targets (ms) — used for logging/alerting
+SLA_TARGETS_MS: dict[Priority, int] = {
+    "low":      2000,   # 2 seconds acceptable
+    "medium":   1000,   # 1 second
+    "high":     500,    # 500ms
+    "critical": 200,    # 200ms — only local can reliably hit this
+}
+
+
+@dataclass
+class RouterDecision:
+    """The routing decision made for a single request."""
+    task_type: str
+    priority: str
+    model_tier: ModelTier
+    model_name: str
+    sla_target_ms: int
+    reasoning: str
+
+
+@dataclass
+class RouterMetrics:
+    """Aggregated metrics across all router calls (for Prometheus/Grafana)."""
+    total_calls: int = 0
+    local_calls: int = 0
+    cloud_calls: int = 0
+    total_cost_usd: float = 0.0
+    total_latency_ms: float = 0.0
+    sla_violations: int = 0
+    errors: int = 0
+    calls_by_task: dict = field(default_factory=lambda: defaultdict(int))
+    calls_by_priority: dict = field(default_factory=lambda: defaultdict(int))
+
+    @property
+    def avg_latency_ms(self) -> float:
+        return self.total_latency_ms / self.total_calls if self.total_calls else 0.0
+
+    @property
+    def local_pct(self) -> float:
+        return (self.local_calls / self.total_calls * 100) if self.total_calls else 0.0
+
+    @property
+    def cloud_pct(self) -> float:
+        return (self.cloud_calls / self.total_calls * 100) if self.total_calls else 0.0
+
+
+class LLMRouter:
+    """
+    SLA-Aware Multi-Model LLM Router.
+
+    Automatically selects local or cloud model based on task type and ticket
+    priority. Tracks latency, cost, and SLA compliance for every call.
+
+    Usage:
+        router = LLMRouter()
+
+        response, decision = router.route(
+            messages=[{"role": "user", "content": "Classify this ticket: ..."}],
+            task_type="classify",
+            priority="medium",
+        )
+        print(f"Used: {decision.model_name} ({decision.model_tier})")
+        print(f"Latency: {response.latency_ms}ms")
+        print(f"Cost: ${response.estimated_cost_usd:.4f}")
+    """
+
+    def __init__(self, client: Optional[LLMClient] = None):
+        self.client = client or get_client()
+        self.metrics = RouterMetrics()
+        self._call_log: list[dict] = []
+
+    def _decide(self, task_type: str, priority: str) -> RouterDecision:
+        """
+        Determine which model tier to use based on routing table.
+        Falls back to local if the combination is not in the table.
+        """
+        tier = ROUTING_TABLE.get((task_type, priority), "local")
+        model_name = self.client.local_model if tier == "local" else self.client.cloud_model
+        sla_ms = SLA_TARGETS_MS.get(priority, 2000)
+
+        # Human-readable explanation for audit/debugging
+        if tier == "local":
+            reasoning = (
+                f"Task '{task_type}' at priority '{priority}' routed to LOCAL model "
+                f"({self.client.local_model}) — fast, free, data stays on-premise."
+            )
+        else:
+            reasoning = (
+                f"Task '{task_type}' at priority '{priority}' routed to CLOUD model "
+                f"({self.client.cloud_model}) — frontier reasoning required for "
+                f"high-stakes or action-taking tasks."
+            )
+
+        return RouterDecision(
+            task_type=task_type,
+            priority=priority,
+            model_tier=tier,
+            model_name=model_name,
+            sla_target_ms=sla_ms,
+            reasoning=reasoning,
+        )
+
+    def route(
+        self,
+        messages: list[dict],
+        task_type: TaskType,
+        priority: Priority,
+        temperature: float = 0.1,
+        max_tokens: int = 512,
+    ) -> tuple[LLMResponse, RouterDecision]:
+        """
+        Main routing entry point.
+
+        Decides which model to use, calls it, updates metrics, logs the decision,
+        and checks SLA compliance. Returns (LLMResponse, RouterDecision).
+        """
+        decision = self._decide(task_type, priority)
+
+        logger.info(
+            "ROUTER → %s | task=%s priority=%s | %s",
+            decision.model_tier.upper(), task_type, priority, decision.model_name
+        )
+
+        # Execute the call
+        if decision.model_tier == "local":
+            response = self.client.call_local(
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        else:
+            response = self.client.call_cloud(
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+
+        # Update metrics
+        self.metrics.total_calls += 1
+        self.metrics.calls_by_task[task_type] += 1
+        self.metrics.calls_by_priority[priority] += 1
+
+        if response.success:
+            self.metrics.total_latency_ms += response.latency_ms
+            self.metrics.total_cost_usd += response.estimated_cost_usd
+
+            if decision.model_tier == "local":
+                self.metrics.local_calls += 1
+            else:
+                self.metrics.cloud_calls += 1
+
+            # SLA check
+            if response.latency_ms > decision.sla_target_ms:
+                self.metrics.sla_violations += 1
+                logger.warning(
+                    "SLA VIOLATION: task=%s priority=%s latency=%.0fms target=%dms",
+                    task_type, priority, response.latency_ms, decision.sla_target_ms
+                )
+        else:
+            self.metrics.errors += 1
+            logger.error(
+                "LLM call FAILED: task=%s priority=%s error=%s",
+                task_type, priority, response.error
+            )
+
+        # Append to call log
+        self._call_log.append({
+            "task_type": task_type,
+            "priority": priority,
+            "model_tier": decision.model_tier,
+            "model_name": response.model_used,
+            "latency_ms": response.latency_ms,
+            "cost_usd": response.estimated_cost_usd,
+            "success": response.success,
+            "sla_target_ms": decision.sla_target_ms,
+            "sla_met": response.latency_ms <= decision.sla_target_ms,
+        })
+
+        return response, decision
+
+    def get_metrics_summary(self) -> dict:
+        """Return a dict of aggregated metrics suitable for Prometheus export."""
+        return {
+            "total_calls": self.metrics.total_calls,
+            "local_calls": self.metrics.local_calls,
+            "cloud_calls": self.metrics.cloud_calls,
+            "local_pct": round(self.metrics.local_pct, 1),
+            "cloud_pct": round(self.metrics.cloud_pct, 1),
+            "total_cost_usd": round(self.metrics.total_cost_usd, 4),
+            "avg_latency_ms": round(self.metrics.avg_latency_ms, 1),
+            "sla_violations": self.metrics.sla_violations,
+            "errors": self.metrics.errors,
+            "calls_by_task": dict(self.metrics.calls_by_task),
+            "calls_by_priority": dict(self.metrics.calls_by_priority),
+        }
+
+    def predict_route(self, task_type: str, priority: str) -> RouterDecision:
+        """
+        Predict routing decision WITHOUT making an LLM call.
+        Useful for UI display, testing, and cost estimation.
+        """
+        return self._decide(task_type, priority)
+
+
+# ── Module-level singleton ─────────────────────────────────────────────────────
+_default_router: Optional[LLMRouter] = None
+
+
+def get_router() -> LLMRouter:
+    global _default_router
+    if _default_router is None:
+        _default_router = LLMRouter()
+    return _default_router
+
+
+# ── Standalone Demo ────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.WARNING)
+
+    router = LLMRouter()
+
+    print("=" * 65)
+    print("CustomerCore Phase 7 — SLA-Aware LLM Router Demo")
+    print("(Routing decisions only — no actual LLM calls in dry-run)")
+    print("=" * 65)
+
+    test_cases = [
+        ("classify", "low",      "Classify this support ticket: 'I forgot my password'"),
+        ("classify", "medium",   "Classify this ticket: 'API returning 500 errors on checkout'"),
+        ("classify", "high",     "Classify urgency: 'Production database is down for 200 users'"),
+        ("classify", "critical", "Classify: 'ALL services offline - complete outage for enterprise'"),
+        ("extract",  "medium",   "Extract customer ID and error code from this ticket"),
+        ("reason",   "low",      "Reason about whether this ticket needs escalation"),
+        ("reason",   "medium",   "Determine if SLA is at risk for this ticket"),
+        ("reason",   "high",     "Reason: Should we proactively offer a refund for this outage?"),
+        ("reason",   "critical", "Critical: Is this a coordinated security breach across tenants?"),
+        ("action",   "low",      "Send automated acknowledgment email to customer"),
+        ("action",   "medium",   "Create Jira ticket and assign to billing team"),
+        ("action",   "high",     "Issue $150 partial refund and update HubSpot CRM"),
+        ("action",   "critical", "Escalate to VIP team + notify on-call engineer immediately"),
+    ]
+
+    print(f"\n{'Task':<10} {'Priority':<10} {'-> Tier':<8} {'Model':<35} {'SLA Target'}")
+    print("─" * 90)
+
+    local_count = 0
+    cloud_count = 0
+
+    for task_type, priority, _ in test_cases:
+        decision = router.predict_route(task_type, priority)
+        tier_display = f"{'🟢 LOCAL' if decision.model_tier == 'local' else '🔵 CLOUD'}"
+        model_short = decision.model_name.split("/")[-1][:32]
+        print(
+            f"{task_type:<10} {priority:<10} {tier_display:<14} {model_short:<35} "
+            f"≤{decision.sla_target_ms}ms"
+        )
+        if decision.model_tier == "local":
+            local_count += 1
+        else:
+            cloud_count += 1
+
+    total = len(test_cases)
+    print("─" * 90)
+    print(f"\nRouting summary: {local_count}/{total} LOCAL (${0:.2f} cost) | "
+          f"{cloud_count}/{total} CLOUD (frontier reasoning)")
+    print(f"\nCost savings vs. always-cloud: "
+          f"~{local_count/total*100:.0f}% of calls are free")
+
+    # Verify critical routing rules
+    print("\n" + "─" * 40)
+    print("Verifying critical routing rules:")
+    rules = [
+        (("action", "low"),       "cloud",  "ALL actions must use cloud"),
+        (("action", "critical"),  "cloud",  "ALL actions must use cloud"),
+        (("reason", "critical"),  "cloud",  "Critical reasoning must use cloud"),
+        (("reason", "high"),      "cloud",  "High-priority reasoning must use cloud"),
+        (("classify", "critical"),"local",  "Classification always local"),
+        (("extract", "high"),     "local",  "Extraction always local"),
+        (("reason", "low"),       "local",  "Low-priority reasoning is local"),
+    ]
+    all_ok = True
+    for (task, prio), expected_tier, desc in rules:
+        d = router.predict_route(task, prio)
+        ok = d.model_tier == expected_tier
+        icon = "✓" if ok else "✗"
+        print(f"  {icon} {desc}: {task}/{prio} → {d.model_tier} (expected {expected_tier})")
+        if not ok:
+            all_ok = False
+
+    print(f"\n{'All routing rules VERIFIED ✓' if all_ok else 'ROUTING RULE FAILURES!'}")
+    print("=" * 65)

src/responsible_ai/__init__.py

@@ -0,0 +1,10 @@
+"""
+src/responsible_ai/__init__.py
+
+CustomerCore Responsible AI Package.
+Provides EU AI Act / GDPR compliance modules:
+  - privacy_vault:  Cryptographic AES-256 PII token vault
+  - key_manager:    Tenant-specific encryption key store
+  - audit_log:      Immutable prediction & decryption audit trail
+  - policy_engine:  Declarative per-tenant brand/legal policy enforcement
+"""

src/responsible_ai/audit_log.py

@@ -0,0 +1,124 @@
+"""
+src/responsible_ai/audit_log.py
+
+Immutable audit logger for the CustomerCore Privacy Vault.
+
+Every encrypt and decrypt operation is written here with:
+  - tenant_id, field_name, token
+  - action: "ENCRYPT" or "DECRYPT"
+  - actor_role (for DECRYPT calls — who triggered the re-identification)
+  - timestamp (UTC ISO-8601)
+  - SHA-256 hash of the plaintext (for compliance verification without storing PII)
+
+In local dev: logs are written to a rotating JSON-lines file under logs/.
+In production: rows are inserted to a Supabase PostgreSQL table via the REST API.
+
+Run standalone check:
+  python -m src.responsible_ai.audit_log
+"""
+
+import hashlib
+import json
+import logging
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Literal
+
+# ── Config ────────────────────────────────────────────────────────────────────
+LOG_DIR = Path("logs/vault_audit")
+LOG_DIR.mkdir(parents=True, exist_ok=True)
+AUDIT_FILE = LOG_DIR / "audit_trail.jsonl"
+
+# Configure Python logger for console output too
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [AUDIT] %(message)s",
+    datefmt="%Y-%m-%dT%H:%M:%SZ",
+)
+_log = logging.getLogger("customercore.audit")
+
+
+ActionType = Literal["ENCRYPT", "DECRYPT", "DECRYPT_DENIED"]
+
+
+def _sha256(text: str) -> str:
+    """Return SHA-256 hex digest of a UTF-8 string."""
+    return hashlib.sha256(text.encode("utf-8")).hexdigest()
+
+
+def write_audit_entry(
+    *,
+    tenant_id: str,
+    field_name: str,
+    token: str,
+    action: ActionType,
+    plaintext_hash: str,
+    actor_role: str = "PIPELINE",
+    ticket_id: str = "",
+    extra: dict | None = None,
+) -> dict:
+    """
+    Write a single audit entry to the local JSONL file.
+
+    Returns the entry dict so callers can inspect it in tests.
+    """
+    entry = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "action": action,
+        "tenant_id": tenant_id,
+        "field_name": field_name,
+        "token": token,
+        "plaintext_sha256": plaintext_hash,
+        "actor_role": actor_role,
+        "ticket_id": ticket_id,
+        **(extra or {}),
+    }
+
+    # Write to local JSONL file (append-only)
+    with AUDIT_FILE.open("a", encoding="utf-8") as f:
+        f.write(json.dumps(entry) + "\n")
+
+    # Console log (less detail to avoid accidentally logging sensitive data)
+    _log.info(
+        "[%s] tenant=%s field=%s token=%s role=%s",
+        action, tenant_id, field_name, token[:12] + "...", actor_role,
+    )
+
+    return entry
+
+
+def tail_audit_log(n: int = 10) -> list[dict]:
+    """Return the last n entries from the audit log."""
+    if not AUDIT_FILE.exists():
+        return []
+    lines = AUDIT_FILE.read_text(encoding="utf-8").strip().splitlines()
+    return [json.loads(line) for line in lines[-n:]]
+
+
+if __name__ == "__main__":
+    # Quick standalone test
+    e = write_audit_entry(
+        tenant_id="acme-corp",
+        field_name="EMAIL_ADDRESS",
+        token="TOK_EMAIL_ab12cd34",
+        action="ENCRYPT",
+        plaintext_hash=_sha256("john.doe@acme.com"),
+        ticket_id="TKT-99999",
+    )
+    print("Wrote ENCRYPT entry:")
+    print(json.dumps(e, indent=2))
+
+    d = write_audit_entry(
+        tenant_id="acme-corp",
+        field_name="EMAIL_ADDRESS",
+        token="TOK_EMAIL_ab12cd34",
+        action="DECRYPT",
+        plaintext_hash=_sha256("john.doe@acme.com"),
+        actor_role="SUPPORT_LEAD",
+        ticket_id="TKT-99999",
+    )
+    print("\nWrote DECRYPT entry:")
+    print(json.dumps(d, indent=2))
+
+    print(f"\nLast 2 audit entries: {len(tail_audit_log(2))}")

src/responsible_ai/key_manager.py

@@ -0,0 +1,101 @@
+"""
+src/responsible_ai/key_manager.py
+
+Tenant-specific AES-256 encryption key manager.
+
+Each tenant gets a unique, deterministic Fernet key derived from a
+master secret + their tenant_id via HMAC-SHA256. This guarantees:
+  - Cross-tenant isolation: encrypting with Tenant A's key produces
+    ciphertext that Tenant B's key CANNOT decrypt.
+  - Determinism: the same tenant_id + master secret always produces
+    the same key, so vault tokens remain valid across restarts without
+    persisting individual keys.
+  - Zero plaintext key storage: keys are never written to disk.
+
+In production, swap MASTER_SECRET with a value injected by Doppler.
+In cloud, use Google Cloud KMS or AWS KMS as the key derivation backend.
+
+Run standalone check:
+  python -m src.responsible_ai.key_manager
+"""
+
+import hashlib
+import hmac
+import os
+import base64
+from typing import Optional
+
+# ── Master Secret ─────────────────────────────────────────────────────────────
+# Injected by Doppler in production. Falls back to a safe local dev value.
+# WARNING: Never hardcode a real secret in production code.
+_MASTER_SECRET: str = os.environ.get(
+    "VAULT_MASTER_SECRET",
+    "customercore-local-dev-secret-changeme-in-prod"
+)
+
+
+class TenantKeyManager:
+    """
+    Derives and caches per-tenant AES-256 Fernet keys from a master secret.
+
+    Usage:
+        km = TenantKeyManager()
+        key = km.get_key("acme-corp")          # returns bytes (URL-safe base64)
+        key_same = km.get_key("acme-corp")     # same key, cached
+        key_diff = km.get_key("globex-inc")    # different key, isolated
+    """
+
+    def __init__(self, master_secret: Optional[str] = None):
+        self._secret = (master_secret or _MASTER_SECRET).encode()
+        self._cache: dict[str, bytes] = {}
+
+    def _derive(self, tenant_id: str) -> bytes:
+        """
+        Derive a 32-byte key from HMAC-SHA256(master_secret, tenant_id).
+        Then base64url-encode it so Fernet can consume it directly.
+        """
+        raw = hmac.new(self._secret, tenant_id.encode(), hashlib.sha256).digest()
+        # Fernet requires URL-safe base64-encoded 32-byte key
+        return base64.urlsafe_b64encode(raw)
+
+    def get_key(self, tenant_id: str) -> bytes:
+        """Return the deterministic encryption key for a given tenant."""
+        if tenant_id not in self._cache:
+            self._cache[tenant_id] = self._derive(tenant_id)
+        return self._cache[tenant_id]
+
+    def rotate_key(self, tenant_id: str) -> bytes:
+        """
+        Force re-derive and cache a new key for a tenant.
+        NOTE: Existing encrypted tokens become unreadable after rotation.
+        In production, run a migration to re-encrypt all vault entries first.
+        """
+        key = self._derive(tenant_id)
+        self._cache[tenant_id] = key
+        return key
+
+
+# ── Module-level singleton ────────────────────────────────────────────────────
+_default_manager: Optional[TenantKeyManager] = None
+
+
+def get_key_manager() -> TenantKeyManager:
+    """Return the module-level singleton TenantKeyManager."""
+    global _default_manager
+    if _default_manager is None:
+        _default_manager = TenantKeyManager()
+    return _default_manager
+
+
+if __name__ == "__main__":
+    km = TenantKeyManager()
+    t1 = km.get_key("acme-corp")
+    t2 = km.get_key("globex-inc")
+    t1_again = km.get_key("acme-corp")
+
+    print("=== TenantKeyManager Verification ===")
+    print(f"acme-corp key (1st call) : {t1[:20]}...")
+    print(f"acme-corp key (2nd call) : {t1_again[:20]}... [must match above]")
+    print(f"globex-inc key           : {t2[:20]}...")
+    print(f"Keys are isolated        : {t1 != t2}")
+    print(f"Key is deterministic     : {t1 == t1_again}")

src/responsible_ai/privacy_vault.py

@@ -0,0 +1,442 @@
+"""
+src/responsible_ai/privacy_vault.py
+
+Zero-Trust Cryptographic Privacy Vault for CustomerCore.
+
+== What this replaces ==
+The previous approach (Presidio simple masking) permanently deleted PII from
+ticket text. This meant human agents reviewing low-confidence tickets during
+Human-in-the-Loop pauses could only see [EMAIL] and [NAME] — making triage
+impossible without contacting the customer separately.
+
+== What this does instead ==
+Instead of destroying PII, we:
+  1. Detect PII spans using Presidio (same as before)
+  2. Encrypt each detected value with the tenant's AES-256 Fernet key
+  3. Replace the raw value in the ticket text with a readable token
+     e.g. "john.doe@acme.com" → "<<TOK_EMAIL_ADDRESS_a1b2c3d4>>"
+  4. Store the (token → encrypted_value) pair in the vault database
+  5. Write an audit entry for every encrypt and decrypt action
+
+When a SUPPORT_LEAD or SECURITY_ADMIN needs to review the ticket, they call
+decrypt_token() which re-identifies the field in their UI. Every decrypt is
+logged for GDPR audit compliance.
+
+== GDPR Compliance (Germany / EU) ==
+  GDPR Article 32: Requires "appropriate technical measures" to protect
+  personal data — AES-256 encryption is the gold standard.
+  GDPR Article 17 (Right to be Forgotten): To delete a customer's data,
+  delete their vault entries AND shred the tenant's encryption key.
+  Their Silver Parquet records become permanently unreadable ciphertext.
+
+== Architecture ==
+  CryptographicPrivacyVault
+    ├── TenantKeyManager      — per-tenant AES-256 key derivation
+    ├── VaultStore            — in-memory + SQLite backend for tokens
+    └── AuditLog              — JSONL append-only compliance trail
+
+Run standalone demo:
+  python -m src.responsible_ai.privacy_vault
+"""
+
+import os
+import re
+import sqlite3
+import hashlib
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+from cryptography.fernet import Fernet, InvalidToken
+
+from src.responsible_ai.key_manager import TenantKeyManager, get_key_manager
+from src.responsible_ai.audit_log import write_audit_entry, _sha256
+
+# ── Config ─────────────────────────────────────────────────────────────────────
+VAULT_DB_PATH = Path("logs/vault_audit/vault.db")
+VAULT_DB_PATH.parent.mkdir(parents=True, exist_ok=True)
+
+# Roles allowed to call decrypt
+AUTHORIZED_DECRYPT_ROLES = {"SUPPORT_LEAD", "SECURITY_ADMIN"}
+
+# Token format: <<TOK_{FIELD}_{8-hex-chars}>>
+TOKEN_PATTERN = re.compile(r"<<TOK_[A-Z_]+_[0-9a-f]{8}>>")
+
+
+# ── Vault Store (SQLite) ───────────────────────────────────────────────────────
+
+class VaultStore:
+    """
+    SQLite-backed store mapping (tenant_id, token) → encrypted_value.
+
+    In production, swap with a Supabase PostgreSQL table:
+      supabase.table("pii_vault").insert({...}).execute()
+
+    The SQLite DB is stored at logs/vault_audit/vault.db — never commit this.
+    """
+
+    def __init__(self, db_path: Path = VAULT_DB_PATH):
+        self.db_path = db_path
+        self._conn = sqlite3.connect(str(db_path), check_same_thread=False)
+        self._init_schema()
+
+    def _init_schema(self):
+        self._conn.execute("""
+            CREATE TABLE IF NOT EXISTS pii_vault (
+                token           TEXT NOT NULL,
+                tenant_id       TEXT NOT NULL,
+                field_name      TEXT NOT NULL,
+                encrypted_value TEXT NOT NULL,
+                plaintext_sha256 TEXT NOT NULL,
+                created_at      TEXT NOT NULL,
+                PRIMARY KEY (tenant_id, token)
+            )
+        """)
+        self._conn.commit()
+
+    def store(self, tenant_id: str, token: str, field_name: str,
+              encrypted_value: str, plaintext_sha256: str):
+        self._conn.execute(
+            """INSERT OR REPLACE INTO pii_vault
+               (token, tenant_id, field_name, encrypted_value, plaintext_sha256, created_at)
+               VALUES (?, ?, ?, ?, ?, ?)""",
+            (token, tenant_id, field_name, encrypted_value,
+             plaintext_sha256, datetime.now(timezone.utc).isoformat()),
+        )
+        self._conn.commit()
+
+    def fetch(self, tenant_id: str, token: str) -> Optional[tuple[str, str]]:
+        """Return (field_name, encrypted_value) or None if not found."""
+        row = self._conn.execute(
+            "SELECT field_name, encrypted_value FROM pii_vault WHERE tenant_id=? AND token=?",
+            (tenant_id, token),
+        ).fetchone()
+        return row  # (field_name, encrypted_value) or None
+
+    def delete_tenant(self, tenant_id: str) -> int:
+        """
+        GDPR Right to be Forgotten: delete ALL vault entries for a tenant.
+        Combined with key shredding, their encrypted tokens become permanently
+        unreadable ciphertext in the Silver Parquet files.
+        Returns count of deleted rows.
+        """
+        cur = self._conn.execute(
+            "DELETE FROM pii_vault WHERE tenant_id=?", (tenant_id,)
+        )
+        self._conn.commit()
+        return cur.rowcount
+
+    def count(self, tenant_id: Optional[str] = None) -> int:
+        if tenant_id:
+            return self._conn.execute(
+                "SELECT COUNT(*) FROM pii_vault WHERE tenant_id=?", (tenant_id,)
+            ).fetchone()[0]
+        return self._conn.execute("SELECT COUNT(*) FROM pii_vault").fetchone()[0]
+
+
+# ── Core Vault ─────────────────────────────────────────────────────────────────
+
+class CryptographicPrivacyVault:
+    """
+    Zero-Trust Privacy Vault.
+
+    encrypt_field()  — encrypt a single PII value, return its token
+    encrypt_text()   — detect + encrypt all PII in a full text block
+    decrypt_token()  — decrypt a token back to plaintext (RBAC-enforced)
+    forget_tenant()  — GDPR deletion cascade for all tokens of a tenant
+    """
+
+    def __init__(
+        self,
+        key_manager: Optional[TenantKeyManager] = None,
+        store: Optional[VaultStore] = None,
+    ):
+        self.km = key_manager or get_key_manager()
+        self.store = store or VaultStore()
+
+    # ── Internal helpers ───────────────────────────────────────────────────────
+
+    def _make_token(self, field_name: str, plaintext: str) -> str:
+        """
+        Generate a deterministic, stable token for a PII value.
+        Token format: <<TOK_{FIELD_NAME}_{first8hex_of_sha256}>>
+        Deterministic so the same email always produces the same token,
+        avoiding duplicate vault entries for repeated values.
+        """
+        digest = _sha256(plaintext)[:8]
+        clean_field = field_name.upper().replace(" ", "_")
+        return f"<<TOK_{clean_field}_{digest}>>"
+
+    def _get_cipher(self, tenant_id: str) -> Fernet:
+        key = self.km.get_key(tenant_id)
+        return Fernet(key)
+
+    # ── Public API ─────────────────────────────────────────────────────────────
+
+    def encrypt_field(
+        self,
+        *,
+        tenant_id: str,
+        field_name: str,
+        plaintext: str,
+        ticket_id: str = "",
+    ) -> str:
+        """
+        Encrypt a single PII value. Store in vault. Return its token.
+
+        If this exact plaintext was already encrypted for this tenant,
+        returns the existing token (idempotent — no duplicate vault entries).
+        """
+        token = self._make_token(field_name, plaintext)
+
+        # Idempotency check — already in vault?
+        existing = self.store.fetch(tenant_id, token)
+        if existing:
+            return token  # already stored, token is stable
+
+        # Encrypt with tenant key
+        cipher = self._get_cipher(tenant_id)
+        encrypted = cipher.encrypt(plaintext.encode("utf-8")).decode("utf-8")
+        sha = _sha256(plaintext)
+
+        # Persist to vault
+        self.store.store(tenant_id, token, field_name, encrypted, sha)
+
+        # Write audit entry
+        write_audit_entry(
+            tenant_id=tenant_id,
+            field_name=field_name,
+            token=token,
+            action="ENCRYPT",
+            plaintext_hash=sha,
+            ticket_id=ticket_id,
+        )
+
+        return token
+
+    def encrypt_text(
+        self,
+        *,
+        tenant_id: str,
+        text: str,
+        analyzer,
+        anonymizer,
+        ticket_id: str = "",
+        pii_entities: list[str] | None = None,
+    ) -> tuple[str, int]:
+        """
+        Detect all PII spans in `text` using Presidio, encrypt each one,
+        and replace the raw span with its vault token in the returned string.
+
+        Returns (tokenized_text, count_of_pii_detected).
+        Falls back to standard Presidio anonymization if vault is disabled.
+        """
+        if not text or not isinstance(text, str):
+            return text, 0
+
+        entities = pii_entities or [
+            "EMAIL_ADDRESS", "PHONE_NUMBER", "CREDIT_CARD",
+            "PERSON", "IBAN_CODE", "IP_ADDRESS", "US_SSN",
+        ]
+
+        results = analyzer.analyze(text=text, language="en", entities=entities)
+        if not results:
+            return text, 0
+
+        # Sort by start position descending so we can replace without offset shift
+        results_sorted = sorted(results, key=lambda r: r.start, reverse=True)
+        tokenized = text
+
+        for result in results_sorted:
+            raw_value = text[result.start:result.end]
+            token = self.encrypt_field(
+                tenant_id=tenant_id,
+                field_name=result.entity_type,
+                plaintext=raw_value,
+                ticket_id=ticket_id,
+            )
+            tokenized = tokenized[:result.start] + token + tokenized[result.end:]
+
+        return tokenized, len(results_sorted)
+
+    def decrypt_token(
+        self,
+        *,
+        tenant_id: str,
+        token: str,
+        actor_role: str,
+        ticket_id: str = "",
+    ) -> str:
+        """
+        Decrypt a vault token back to its original plaintext value.
+
+        Role-Based Access Control: only SUPPORT_LEAD and SECURITY_ADMIN
+        may call this. All decrypt calls are logged in the audit trail.
+        Raises PermissionError for unauthorized roles.
+        Raises KeyError if token not found in vault.
+        Raises ValueError if decryption fails (wrong key or corrupted data).
+        """
+        # RBAC check
+        if actor_role not in AUTHORIZED_DECRYPT_ROLES:
+            write_audit_entry(
+                tenant_id=tenant_id,
+                field_name="UNKNOWN",
+                token=token,
+                action="DECRYPT_DENIED",
+                plaintext_hash="",
+                actor_role=actor_role,
+                ticket_id=ticket_id,
+            )
+            raise PermissionError(
+                f"Role '{actor_role}' is not authorized to decrypt vault tokens. "
+                f"Required: {AUTHORIZED_DECRYPT_ROLES}"
+            )
+
+        # Lookup in vault
+        row = self.store.fetch(tenant_id, token)
+        if not row:
+            raise KeyError(
+                f"Token '{token}' not found in vault for tenant '{tenant_id}'."
+            )
+        field_name, encrypted_value = row
+
+        # Decrypt
+        cipher = self._get_cipher(tenant_id)
+        try:
+            plaintext = cipher.decrypt(encrypted_value.encode("utf-8")).decode("utf-8")
+        except InvalidToken as e:
+            raise ValueError(
+                f"Failed to decrypt token '{token}' — key may have been rotated: {e}"
+            ) from e
+
+        # Audit the successful decrypt
+        write_audit_entry(
+            tenant_id=tenant_id,
+            field_name=field_name,
+            token=token,
+            action="DECRYPT",
+            plaintext_hash=_sha256(plaintext),
+            actor_role=actor_role,
+            ticket_id=ticket_id,
+        )
+
+        return plaintext
+
+    def decrypt_text(
+        self,
+        *,
+        tenant_id: str,
+        text: str,
+        actor_role: str,
+        ticket_id: str = "",
+    ) -> str:
+        """
+        Find all vault tokens in `text` and replace them with decrypted values.
+        Requires an authorized role. Each token decrypt is individually audited.
+        """
+        tokens_found = TOKEN_PATTERN.findall(text)
+        result = text
+        for token in set(tokens_found):
+            try:
+                plaintext = self.decrypt_token(
+                    tenant_id=tenant_id,
+                    token=token,
+                    actor_role=actor_role,
+                    ticket_id=ticket_id,
+                )
+                result = result.replace(token, plaintext)
+            except (KeyError, ValueError):
+                # Token not found or corrupted — leave as-is
+                pass
+        return result
+
+    def forget_tenant(self, tenant_id: str) -> dict:
+        """
+        GDPR Article 17 — Right to be Forgotten.
+
+        Deletes ALL vault entries for a tenant. After this call, their encrypted
+        tokens in Silver Parquet files become permanently unreadable ciphertext.
+        Callers should also shred the tenant's key via key_manager.rotate_key().
+
+        Returns a summary of what was deleted.
+        """
+        deleted = self.store.delete_tenant(tenant_id)
+        write_audit_entry(
+            tenant_id=tenant_id,
+            field_name="ALL",
+            token="ALL",
+            action="ENCRYPT",  # closest valid action — log the deletion event
+            plaintext_hash="GDPR_DELETION",
+            actor_role="GDPR_PROCESSOR",
+            extra={"event": "GDPR_FORGET_TENANT", "deleted_rows": deleted},
+        )
+        return {"tenant_id": tenant_id, "vault_entries_deleted": deleted}
+
+
+# ── Module-level singleton ─────────────────────────────────────────────────────
+_default_vault: Optional[CryptographicPrivacyVault] = None
+
+
+def get_vault() -> CryptographicPrivacyVault:
+    """Return the module-level singleton vault (shared key manager + store)."""
+    global _default_vault
+    if _default_vault is None:
+        _default_vault = CryptographicPrivacyVault()
+    return _default_vault
+
+
+# ── Standalone Demo ────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    from presidio_analyzer import AnalyzerEngine
+    from presidio_anonymizer import AnonymizerEngine
+    from presidio_analyzer.nlp_engine import NlpEngineProvider
+
+    nlp_cfg = {"nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}]}
+    nlp_engine = NlpEngineProvider(nlp_configuration=nlp_cfg).create_engine()
+    analyzer = AnalyzerEngine(nlp_engine=nlp_engine)
+    anonymizer = AnonymizerEngine()
+
+    vault = CryptographicPrivacyVault()
+    tenant = "acme-corp"
+    ticket_text = (
+        "Hi, my name is John Smith and I can be reached at john.smith@acme.com "
+        "or call +1 212-555-9876. My credit card 4111-1111-1111-1111 was charged twice."
+    )
+
+    print("=" * 60)
+    print("CustomerCore Cryptographic Privacy Vault — Demo")
+    print("=" * 60)
+    print(f"\nOriginal text:\n  {ticket_text}")
+
+    tokenized, count = vault.encrypt_text(
+        tenant_id=tenant,
+        text=ticket_text,
+        analyzer=analyzer,
+        anonymizer=anonymizer,
+        ticket_id="TKT-DEMO-001",
+    )
+    print(f"\nTokenized text ({count} PII entities detected):\n  {tokenized}")
+    print(f"\nVault entries stored: {vault.store.count(tenant)}")
+
+    # Authorized decrypt
+    restored = vault.decrypt_text(
+        tenant_id=tenant,
+        text=tokenized,
+        actor_role="SUPPORT_LEAD",
+        ticket_id="TKT-DEMO-001",
+    )
+    print(f"\nRestored text (SUPPORT_LEAD view):\n  {restored}")
+
+    # Unauthorized decrypt
+    print("\nAttempting decrypt with AGENT role (unauthorized)...")
+    try:
+        vault.decrypt_token(
+            tenant_id=tenant,
+            token=list(TOKEN_PATTERN.findall(tokenized))[0],
+            actor_role="AGENT",
+        )
+    except PermissionError as e:
+        print(f"  BLOCKED: {e}")
+
+    print(f"\nAudit log written to: logs/vault_audit/audit_trail.jsonl")
+    print("=" * 60)

src/streaming/bronze_consumer.py

@@ -0,0 +1,177 @@
+"""
+src/streaming/bronze_consumer.py
+
+Reads from all 4 Redpanda topics and writes raw messages to MinIO
+as the Bronze layer (unmodified, append-only Parquet files).
+
+Bronze = raw data exactly as received. Nothing is changed.
+If something goes wrong downstream, we always have the original.
+
+Run: python -m src.streaming.bronze_consumer --batch-size 1000
+     Ctrl+C to stop gracefully.
+"""
+
+import argparse
+import io
+import json
+import signal
+import sys
+import time
+from collections import defaultdict
+from datetime import datetime, timezone
+
+import boto3
+import pyarrow as pa
+import pyarrow.parquet as pq
+from botocore.client import Config
+from confluent_kafka import Consumer, KafkaError
+from tqdm import tqdm
+
+# ── Config ────────────────────────────────────────────────────
+BROKER = "localhost:9092"
+GROUP_ID = "bronze-consumer-group"
+TOPICS = ["support-tickets", "billing-events", "product-events", "incident-events"]
+BUCKET = "customercore-lake"
+TOPIC_TO_PREFIX = {
+    "support-tickets": "bronze/tickets",
+    "billing-events": "bronze/billing",
+    "product-events": "bronze/product",
+    "incident-events": "bronze/incidents",
+}
+
+MINIO_ENDPOINT = "http://localhost:9000"
+MINIO_ACCESS_KEY = "minioadmin"
+MINIO_SECRET_KEY = "minioadmin"
+
+running = True
+
+
+def signal_handler(sig, frame):
+    global running
+    print("\n[STOP] Graceful shutdown triggered...")
+    running = False
+
+
+signal.signal(signal.SIGINT, signal_handler)
+
+
+def get_s3():
+    return boto3.client(
+        "s3",
+        endpoint_url=MINIO_ENDPOINT,
+        aws_access_key_id=MINIO_ACCESS_KEY,
+        aws_secret_access_key=MINIO_SECRET_KEY,
+        config=Config(signature_version="s3v4"),
+        region_name="us-east-1",
+    )
+
+
+def write_batch_to_bronze(s3, topic: str, records: list[dict]) -> str:
+    """Write a batch of raw records to MinIO as a Parquet file."""
+    prefix = TOPIC_TO_PREFIX[topic]
+    timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S_%f")
+    key = f"{prefix}/batch_{timestamp}.parquet"
+
+    # Convert to Arrow table — all values as strings to preserve raw format
+    rows = [{"raw_json": json.dumps(r), "ingested_at": datetime.now(timezone.utc).isoformat()} for r in records]
+    table = pa.table({
+        "raw_json": pa.array([r["raw_json"] for r in rows], type=pa.string()),
+        "ingested_at": pa.array([r["ingested_at"] for r in rows], type=pa.string()),
+    })
+
+    buf = io.BytesIO()
+    pq.write_table(table, buf)
+    buf.seek(0)
+    s3.put_object(Bucket=BUCKET, Key=key, Body=buf.getvalue())
+    return key
+
+
+def main(batch_size: int = 500, timeout_seconds: int = 30):
+    print("=" * 60)
+    print("CustomerCore Bronze Consumer")
+    print(f"Topics : {', '.join(TOPICS)}")
+    print(f"Sink   : MinIO s3://{BUCKET}/bronze/")
+    print(f"Batch  : {batch_size} messages per Parquet file")
+    print("Press Ctrl+C to stop gracefully")
+    print("=" * 60)
+
+    consumer = Consumer({
+        "bootstrap.servers": BROKER,
+        "group.id": GROUP_ID,
+        "auto.offset.reset": "earliest",
+        "enable.auto.commit": True,
+    })
+    consumer.subscribe(TOPICS)
+    s3 = get_s3()
+
+    buffers: dict[str, list] = defaultdict(list)
+    total_written = 0
+    files_written = 0
+    start = time.time()
+    last_message_time = time.time()
+
+    print(f"\nListening for messages (will auto-stop after {timeout_seconds}s silence)...\n")
+    pbar = tqdm(unit="msg", desc="Consumed", bar_format="{desc}: {n_fmt} msgs | Files: {postfix}")
+    pbar.set_postfix_str("0")
+
+    try:
+        while running:
+            msg = consumer.poll(timeout=1.0)
+
+            # Auto-stop if silent for timeout_seconds
+            if time.time() - last_message_time > timeout_seconds:
+                print(f"\n[INFO] No messages for {timeout_seconds}s — flushing and stopping.")
+                break
+
+            if msg is None:
+                continue
+            if msg.error():
+                if msg.error().code() != KafkaError._PARTITION_EOF:
+                    print(f"[ERROR] {msg.error()}")
+                continue
+
+            last_message_time = time.time()
+            topic = msg.topic()
+            try:
+                value = json.loads(msg.value().decode())
+                buffers[topic].append(value)
+                total_written += 1
+                pbar.update(1)
+            except json.JSONDecodeError:
+                continue
+
+            # Flush buffer when batch is full
+            if len(buffers[topic]) >= batch_size:
+                key = write_batch_to_bronze(s3, topic, buffers[topic])
+                files_written += 1
+                pbar.set_postfix_str(str(files_written))
+                buffers[topic] = []
+
+        # Flush remaining partial batches
+        print("\nFlushing remaining buffers...")
+        for topic, records in buffers.items():
+            if records:
+                key = write_batch_to_bronze(s3, topic, records)
+                files_written += 1
+                print(f"  [FLUSHED] {len(records)} records -> {key}")
+
+    finally:
+        pbar.close()
+        consumer.close()
+
+    elapsed = time.time() - start
+    print(f"\n{'=' * 60}")
+    print(f"  Total consumed : {total_written:,} messages")
+    print(f"  Parquet files  : {files_written}")
+    print(f"  Duration       : {elapsed:.1f}s")
+    print(f"  Sink           : s3://{BUCKET}/bronze/")
+    print(f"  Browse MinIO   : http://localhost:9001")
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch-size", type=int, default=500, help="Messages per Parquet file")
+    parser.add_argument("--timeout", type=int, default=30, help="Stop after N seconds of silence")
+    args = parser.parse_args()
+    main(batch_size=args.batch_size, timeout_seconds=args.timeout)

src/streaming/bronze_to_silver.py

@@ -0,0 +1,328 @@
+"""
+src/streaming/bronze_to_silver.py
+
+PySpark job that reads Bronze Parquet files from MinIO,
+applies PII masking via Presidio, cleans and validates records,
+and writes to the Silver layer.
+
+Silver = cleaned, validated, PII-masked. Safe to query and use for ML.
+
+PII entities masked: EMAIL_ADDRESS, PHONE_NUMBER, CREDIT_CARD,
+                     PERSON, IBAN_CODE, IP_ADDRESS
+
+Run: python -m src.streaming.bronze_to_silver
+     python -m src.streaming.bronze_to_silver --source tickets --limit 500
+"""
+
+import argparse
+import io
+import json
+import time
+from datetime import datetime, timezone
+from typing import Optional
+
+import boto3
+import pyarrow as pa
+import pyarrow.parquet as pq
+from botocore.client import Config
+from presidio_analyzer import AnalyzerEngine
+from presidio_anonymizer import AnonymizerEngine
+from tqdm import tqdm
+
+# Optional vault import — gracefully degrade if not configured
+try:
+    from src.responsible_ai.privacy_vault import CryptographicPrivacyVault
+    _VAULT_AVAILABLE = True
+except ImportError:
+    _VAULT_AVAILABLE = False
+    CryptographicPrivacyVault = None
+
+# ── Config ────────────────────────────────────────────────────
+MINIO_ENDPOINT = "http://localhost:9000"
+MINIO_ACCESS_KEY = "minioadmin"
+MINIO_SECRET_KEY = "minioadmin"
+BUCKET = "customercore-lake"
+
+SOURCE_MAP = {
+    "tickets": ("bronze/tickets", "silver/tickets"),
+    "billing": ("bronze/billing", "silver/billing"),
+    "product": ("bronze/product", "silver/product"),
+    "incidents": ("bronze/incidents", "silver/incidents"),
+}
+
+PII_ENTITIES = [
+    "EMAIL_ADDRESS", "PHONE_NUMBER", "CREDIT_CARD",
+    "PERSON", "IBAN_CODE", "IP_ADDRESS", "US_SSN",
+]
+
+VALID_PRIORITIES = {"low", "medium", "high", "critical"}
+VALID_TIERS = {"enterprise", "professional", "free"}
+VALID_CHANNELS = {"email", "web", "api", "chat"}
+
+
+def get_s3():
+    return boto3.client(
+        "s3",
+        endpoint_url=MINIO_ENDPOINT,
+        aws_access_key_id=MINIO_ACCESS_KEY,
+        aws_secret_access_key=MINIO_SECRET_KEY,
+        config=Config(signature_version="s3v4"),
+        region_name="us-east-1",
+    )
+
+
+def list_bronze_files(s3, prefix: str) -> list[str]:
+    response = s3.list_objects_v2(Bucket=BUCKET, Prefix=prefix)
+    return [obj["Key"] for obj in response.get("Contents", []) if obj["Key"].endswith(".parquet")]
+
+
+def read_parquet_from_minio(s3, key: str) -> list[dict]:
+    obj = s3.get_object(Bucket=BUCKET, Key=key)
+    buf = io.BytesIO(obj["Body"].read())
+    table = pq.read_table(buf)
+    records = []
+    for row in table.to_pydict()["raw_json"]:
+        try:
+            records.append(json.loads(row))
+        except json.JSONDecodeError:
+            pass
+    return records
+
+
+def mask_pii(text: str, analyzer: AnalyzerEngine, anonymizer: AnonymizerEngine) -> str:
+    """Run Presidio PII detection and replace with entity type placeholder (legacy path)."""
+    if not text or not isinstance(text, str):
+        return text
+    results = analyzer.analyze(text=text, language="en", entities=PII_ENTITIES)
+    if not results:
+        return text
+    anonymized = anonymizer.anonymize(text=text, analyzer_results=results)
+    return anonymized.text
+
+
+def vault_mask_pii(
+    text: str,
+    analyzer: AnalyzerEngine,
+    anonymizer: AnonymizerEngine,
+    vault: "CryptographicPrivacyVault",
+    tenant_id: str,
+    ticket_id: str = "",
+) -> tuple[str, int]:
+    """
+    Vault-backed PII protection (upgraded path).
+    Encrypts each PII span with the tenant's AES-256 key and replaces it
+    with a stable, readable token in the text. Returns (tokenized_text, pii_count).
+    Falls back to plain masking if vault is None.
+    """
+    if vault is None:
+        return mask_pii(text, analyzer, anonymizer), 0
+    return vault.encrypt_text(
+        tenant_id=tenant_id,
+        text=text,
+        analyzer=analyzer,
+        anonymizer=anonymizer,
+        ticket_id=ticket_id,
+        pii_entities=PII_ENTITIES,
+    )
+
+
+def validate_and_clean_ticket(
+    record: dict,
+    analyzer,
+    anonymizer,
+    vault: Optional["CryptographicPrivacyVault"] = None,
+) -> dict | None:
+    """
+    Clean, validate, and PII-protect a ticket record.
+
+    When a CryptographicPrivacyVault is provided (Phase 5+):
+      - PII is encrypted with tenant-specific AES-256 and replaced by stable tokens
+      - Tokens are stored in the vault for authorized re-identification
+      - silver_version is bumped to '2.0' to signal vault-protected records
+
+    Without a vault (Phase 3 legacy / backward-compatible path):
+      - PII is replaced by [ENTITY_TYPE] placeholders (permanent deletion)
+      - silver_version remains '1.0'
+
+    Returns None if the record is missing required fields.
+    """
+    # Required fields
+    if not record.get("event_id") or not record.get("tenant_id"):
+        return None
+    if not record.get("subject") and not record.get("body"):
+        return None
+
+    # Normalize fields
+    priority = str(record.get("priority", "medium")).lower()
+    tier = str(record.get("customer_tier", "free")).lower()
+    channel = str(record.get("channel", "web")).lower()
+    tenant_id = record["tenant_id"]
+    ticket_id = record.get("ticket_id", "")
+
+    # PII protection — vault path (Phase 5) or legacy masking path (Phase 3)
+    if vault is not None:
+        subject_clean, _ = vault_mask_pii(
+            str(record.get("subject", ""))[:256], analyzer, anonymizer,
+            vault, tenant_id, ticket_id,
+        )
+        body_clean, pii_count = vault_mask_pii(
+            str(record.get("body", "")), analyzer, anonymizer,
+            vault, tenant_id, ticket_id,
+        )
+        silver_version = "2.0"  # vault-protected
+    else:
+        subject_clean = mask_pii(str(record.get("subject", ""))[:256], analyzer, anonymizer)
+        body_clean = mask_pii(str(record.get("body", "")), analyzer, anonymizer)
+        silver_version = "1.0"  # legacy masking
+
+    return {
+        "event_id": record["event_id"],
+        "event_type": record.get("event_type", "support_ticket_created"),
+        "tenant_id": tenant_id,
+        "ticket_id": ticket_id,
+        "customer_id": record.get("customer_id", ""),
+        "customer_tier": tier if tier in VALID_TIERS else "free",
+        "subject": subject_clean,
+        "body": body_clean,
+        "category": str(record.get("category", "general")).lower(),
+        "priority": priority if priority in VALID_PRIORITIES else "medium",
+        "channel": channel if channel in VALID_CHANNELS else "web",
+        "reopen_count": max(0, int(record.get("reopen_count", 0))),
+        "tags": record.get("tags", []),
+        "original_timestamp": record.get("timestamp", ""),
+        "processed_at": datetime.now(timezone.utc).isoformat(),
+        "pii_masked": True,
+        "vault_protected": vault is not None,
+        "silver_version": silver_version,
+    }
+
+
+def write_silver(s3, prefix: str, records: list[dict], batch_id: str):
+    """Write cleaned records to Silver layer as Parquet."""
+    if not records:
+        return
+
+    # Build columnar data
+    columns = list(records[0].keys())
+    data = {col: [str(r.get(col, "")) for r in records] for col in columns}
+    # reopen_count is int
+    data["reopen_count"] = [int(r.get("reopen_count", 0)) for r in records]
+
+    table = pa.table({k: pa.array(v) for k, v in data.items()})
+    buf = io.BytesIO()
+    pq.write_table(table, buf, compression="snappy")
+    buf.seek(0)
+
+    key = f"{prefix}/silver_batch_{batch_id}.parquet"
+    s3.put_object(Bucket=BUCKET, Key=key, Body=buf.getvalue())
+    return key
+
+
+def main(source: str = "tickets", limit: int = None, use_vault: bool = True):
+    bronze_prefix, silver_prefix = SOURCE_MAP.get(source, SOURCE_MAP["tickets"])
+
+    print("=" * 60)
+    print("CustomerCore Bronze -> Silver Pipeline")
+    print(f"  Source : s3://{BUCKET}/{bronze_prefix}/")
+    print(f"  Sink   : s3://{BUCKET}/{silver_prefix}/")
+    print(f"  PII masked: {', '.join(PII_ENTITIES)}")
+    print("=" * 60)
+
+    s3 = get_s3()
+
+    # ── 1. Discover Bronze files ──────────────────────────────
+    print("\n[1/5] Scanning Bronze layer for Parquet files...")
+    files = list_bronze_files(s3, bronze_prefix)
+    if not files:
+        print("  No Bronze files found. Run bronze_consumer.py first.")
+        return
+    print(f"  Found {len(files)} Parquet file(s)")
+
+    # ── 2. Load all records ───────────────────────────────────
+    print("\n[2/5] Loading records from Bronze...")
+    t0 = time.time()
+    all_records = []
+    for f in tqdm(files, desc="Reading", unit="file"):
+        all_records.extend(read_parquet_from_minio(s3, f))
+    print(f"  Loaded {len(all_records):,} raw records in {time.time()-t0:.1f}s")
+
+    if limit:
+        all_records = all_records[:limit]
+        print(f"  Limited to {limit:,} records")
+
+    # ── 3. Initialize Presidio + Privacy Vault ───────────────────
+    # Using en_core_web_sm (12MB) — sufficient for PII detection.
+    # en_core_web_lg (400MB) is NOT needed and causes download failures.
+    print("\n[3/5] Initializing Presidio PII engine (en_core_web_sm)...")
+    t0 = time.time()
+    from presidio_analyzer.nlp_engine import NlpEngineProvider
+    nlp_config = {"nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}]}
+    nlp_engine = NlpEngineProvider(nlp_configuration=nlp_config).create_engine()
+    analyzer = AnalyzerEngine(nlp_engine=nlp_engine)
+    anonymizer = AnonymizerEngine()
+    print(f"  Ready in {time.time()-t0:.1f}s")
+
+    # Initialize vault (Phase 5 — Zero-Trust Cryptographic Privacy Vault)
+    vault = None
+    if use_vault and _VAULT_AVAILABLE:
+        from src.responsible_ai.privacy_vault import CryptographicPrivacyVault
+        vault = CryptographicPrivacyVault()
+        print(f"  Privacy Vault: ENABLED (AES-256, silver_version=2.0)")
+    else:
+        print(f"  Privacy Vault: DISABLED (legacy Presidio masking, silver_version=1.0)")
+
+    # ── 4. Clean + mask PII ───────────────────────────────────
+    print(f"\n[4/5] Cleaning and masking PII in {len(all_records):,} records...")
+    print("  Estimated time: ~1-3 minutes for 1000 records (Presidio NER scan)")
+    t0 = time.time()
+    clean_records = []
+    dropped = 0
+
+    with tqdm(total=len(all_records), unit="rec",
+              bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]") as pbar:
+        for record in all_records:
+            if source == "tickets":
+                cleaned = validate_and_clean_ticket(record, analyzer, anonymizer, vault=vault)
+            else:
+                # For non-ticket sources: just add processing metadata, no PII masking needed
+                record["processed_at"] = datetime.now(timezone.utc).isoformat()
+                record["silver_version"] = "1.0"
+                record["vault_protected"] = False
+                cleaned = record
+            if cleaned:
+                clean_records.append(cleaned)
+            else:
+                dropped += 1
+            pbar.update(1)
+
+    elapsed = time.time() - t0
+    rate = len(all_records) / elapsed if elapsed > 0 else float(len(all_records))
+    print(f"\n  Processed : {len(all_records):,} records in {elapsed:.1f}s ({rate:.0f} rec/s)")
+    print(f"  Valid     : {len(clean_records):,}")
+    print(f"  Dropped   : {dropped:,} (missing required fields)")
+
+    # ── 5. Write Silver ───────────────────────────────────────
+    print(f"\n[5/5] Writing {len(clean_records):,} records to Silver layer...")
+    batch_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+    t0 = time.time()
+    key = write_silver(s3, silver_prefix, clean_records, batch_id)
+    print(f"  Written in {time.time()-t0:.1f}s -> {key}")
+
+    print(f"\n{'=' * 60}")
+    print(f"  Bronze records  : {len(all_records):,}")
+    print(f"  Silver records  : {len(clean_records):,}")
+    print(f"  Dropped         : {dropped:,}")
+    print(f"  PII masked      : Yes ({', '.join(PII_ENTITIES)})")
+    print(f"  Output          : s3://{BUCKET}/{silver_prefix}/")
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source", choices=["tickets", "billing", "product", "incidents"],
+                        default="tickets", help="Which Bronze topic to process")
+    parser.add_argument("--limit", type=int, default=None, help="Max records to process")
+    parser.add_argument("--no-vault", action="store_true",
+                        help="Disable Privacy Vault and use legacy Presidio masking")
+    args = parser.parse_args()
+    main(source=args.source, limit=args.limit, use_vault=not args.no_vault)

src/streaming/data_loader.py

@@ -0,0 +1,378 @@
+"""
+src/streaming/data_loader.py
+
+Multi-Dataset CustomerCore Data Loader
+Downloads multiple open-source customer support datasets from Hugging Face
+and publishes enriched events to Redpanda topics with progress tracking.
+
+== Datasets (all open-source, no scraping, no API keys, legal in EU/Germany) ==
+  1. bitext/Bitext-customer-support-llm-chatbot-training-dataset
+       26,872 rows | CDLA-Sharing-1.0 | SaaS customer support Q&A (English)
+       Categories: billing, account, orders, technical, etc.
+
+  2. bitext/Bitext-retail-banking-llm-chatbot-training-dataset
+       25,545 rows | CDLA-Sharing-1.0 | B2B banking/financial support Q&A (English)
+       Categories: card, loan, account, transfer, compliance, etc.
+
+  3. mteb/amazon_massive_intent [de, fr, es]
+       11,514 rows x 3 languages = 34,542 rows | Apache 2.0
+       Real customer voice assistant intent utterances in German, French, Spanish
+       Intents map to: alarm, calendar, email, audio, transport, shopping, etc.
+       Source: Amazon MASSIVE (Fitzgerald et al., 2022)
+
+  Total: ~87,000 rows across 4 languages (en, de, fr, es)
+
+== Multi-Language Strategy ==
+  A B2B SaaS platform serving EU customers receives tickets in German, French,
+  Spanish, Portuguese etc. Single-language support degrades classification accuracy
+  by 15-40% and is not acceptable for GDPR-compliant EU platforms.
+  MASSIVE gives us real customer utterances in each language — not translations.
+
+== Run ==
+  python -m src.streaming.data_loader                            # all sources (default)
+  python -m src.streaming.data_loader --sources customer         # SaaS English only
+  python -m src.streaming.data_loader --sources banking          # Banking English only
+  python -m src.streaming.data_loader --sources massive          # Multilingual only
+  python -m src.streaming.data_loader --sources all              # Everything ~87k rows
+  python -m src.streaming.data_loader --limit 500 --sources all  # Quick test
+"""
+
+import argparse
+import json
+import random
+import time
+import uuid
+from datetime import datetime, timezone
+
+from confluent_kafka import Producer
+from datasets import load_dataset, concatenate_datasets
+from tqdm import tqdm
+
+# ── Config ────────────────────────────────────────────────────
+BROKER = "localhost:9092"
+TOPIC = "support-tickets"
+
+DATASET_CONFIGS = {
+    "customer": {
+        "id": "bitext/Bitext-customer-support-llm-chatbot-training-dataset",
+        "license": "CDLA-Sharing-1.0",
+        "domain": "saas_support",
+        "text_field": "instruction",
+        "response_field": "response",
+        "category_field": "category",
+        "tags_field": "tags",
+        "language": "en",
+    },
+    "banking": {
+        "id": "bitext/Bitext-retail-banking-llm-chatbot-training-dataset",
+        "license": "CDLA-Sharing-1.0",
+        "domain": "financial_support",
+        "text_field": "instruction",
+        "response_field": "response",
+        "category_field": "category",
+        "tags_field": "tags",
+        "language": "en",
+    },
+}
+
+# Multilingual MASSIVE configs (per language — Apache 2.0, no scraping)
+MASSIVE_LANGUAGES = {
+    "de": {"name": "German",   "mteb_config": "de"},
+    "fr": {"name": "French",   "mteb_config": "fr"},
+    "es": {"name": "Spanish",  "mteb_config": "es"},
+}
+
+# ── Enrichment pools (makes data multi-tenant) ────────────────
+TENANTS = ["acme-corp", "globex-inc", "initech-ltd", "umbrella-co", "stark-tech"]
+TIERS = ["enterprise", "professional", "free"]
+CHANNELS = ["email", "web", "api", "chat"]
+
+PRIORITY_MAP = {
+    # SaaS support categories
+    "ACCOUNT": ["medium", "high"],
+    "BILLING": ["high", "high", "critical"],
+    "CANCEL": ["high", "critical"],
+    "CONTACT": ["low", "medium"],
+    "DELIVERY": ["medium", "high"],
+    "FEEDBACK": ["low", "medium"],
+    "INVOICE": ["high", "critical"],
+    "NEWSLETTER": ["low"],
+    "ORDER": ["medium", "high"],
+    "PAYMENT": ["high", "critical"],
+    "REFUND": ["high", "high", "critical"],
+    "REVIEW": ["low", "medium"],
+    "SHIPPING": ["medium", "high"],
+    "SUBSCRIPTION": ["medium", "high"],
+    "SUPPORT": ["medium", "high"],
+    # Banking categories
+    "CARD": ["high", "critical"],
+    "LOAN": ["medium", "high"],
+    "TRANSFER": ["high", "critical"],
+    "COMPLIANCE": ["high", "critical"],
+    "MORTGAGE": ["medium", "high"],
+    "SAVINGS": ["low", "medium"],
+    "FRAUD": ["critical", "critical"],
+}
+
+
+def delivery_report(err, msg):
+    if err is not None:
+        print(f"\n  [ERROR] Delivery failed: {err}")
+
+
+def enrich(row: dict, domain: str, license_str: str) -> dict:
+    """
+    Normalize a raw Bitext row into a CustomerCore ticket event.
+    Both Bitext datasets share the same column schema (instruction / response / category).
+    Domain tag differentiates SaaS support from financial support in downstream analysis.
+    """
+    category = str(row.get("category", "SUPPORT")).upper()
+    tier = random.choice(TIERS)
+    priority_pool = PRIORITY_MAP.get(category[:8], ["low", "medium", "high"])
+    if tier == "enterprise":
+        priority_pool = [p for p in priority_pool if p in ("high", "critical")] or ["high"]
+
+    tags_raw = row.get("tags", "")
+    tags = [t.strip() for t in str(tags_raw).split(",") if t.strip()] if tags_raw else []
+
+    return {
+        "event_id": str(uuid.uuid4()),
+        "event_type": "support_ticket_created",
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "tenant_id": random.choice(TENANTS),
+        "ticket_id": f"TKT-{random.randint(10000, 99999)}",
+        "customer_id": f"CUST-{random.randint(1000, 99999)}",
+        "customer_tier": tier,
+        "subject": str(row.get("instruction", ""))[:120],
+        "body": str(row.get("instruction", "")),
+        "suggested_response": str(row.get("response", "")),
+        "category": category.lower(),
+        "priority": random.choice(priority_pool),
+        "channel": random.choice(CHANNELS),
+        "reopen_count": random.randint(0, 2),
+        "tags": tags,
+        "source_domain": domain,
+        "source": f"huggingface:bitext-{domain}",
+        "license": license_str,
+        "language": "en",
+        "is_multilingual": False,
+    }
+
+
+def enrich_massive(row: dict, language: str, lang_name: str) -> dict:
+    """
+    Normalize an Amazon MASSIVE intent row into a CustomerCore ticket event.
+
+    MASSIVE rows have: id, label, label_text, text, lang
+    label_text = intent name (e.g. 'alarm_set', 'email_query', 'calendar_remove')
+    text = the customer utterance in the target language
+
+    Maps MASSIVE intents to support categories:
+      alarm/reminder/calendar -> account
+      email/messaging         -> account
+      audio/music/podcast     -> product
+      transport/travel        -> order
+      shopping                -> order
+      iot/smart_home          -> technical
+      weather/datetime/news   -> general
+    """
+    INTENT_TO_CATEGORY = {
+        "alarm": "account", "reminder": "account", "calendar": "account",
+        "email": "account", "messaging": "account", "social": "account",
+        "audio": "technical", "music": "technical", "podcast": "technical",
+        "play": "technical", "iot": "technical", "smart": "technical",
+        "transport": "order", "travel": "order", "lists": "order",
+        "shopping": "order", "takeaway": "order", "cooking": "order",
+        "weather": "general", "datetime": "general", "news": "general",
+        "qa": "general", "recommendation": "general", "general": "general",
+    }
+
+    intent = str(row.get("label_text", "general")).lower()
+    category = "general"
+    for key, cat in INTENT_TO_CATEGORY.items():
+        if key in intent:
+            category = cat
+            break
+
+    tier = random.choice(TIERS)
+    priority_pool = ["low", "medium", "medium", "high"]
+
+    return {
+        "event_id": str(uuid.uuid4()),
+        "event_type": "support_ticket_created",
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "tenant_id": random.choice(TENANTS),
+        "ticket_id": f"TKT-{random.randint(10000, 99999)}",
+        "customer_id": f"CUST-{random.randint(1000, 99999)}",
+        "customer_tier": tier,
+        "subject": str(row.get("text", ""))[:120],
+        "body": str(row.get("text", "")),
+        "suggested_response": "",
+        "category": category,
+        "priority": random.choice(priority_pool),
+        "channel": random.choice(CHANNELS),
+        "reopen_count": 0,
+        "tags": [intent.replace("_", "-")],
+        "source_domain": "multilingual_intent",
+        "source": f"huggingface:amazon-massive-{language}",
+        "license": "Apache-2.0",
+        "language": language,
+        "language_name": lang_name,
+        "is_multilingual": True,
+        "intent": intent,
+    }
+
+
+def load_sources(sources: str) -> tuple[list, dict]:
+    """
+    Download and concatenate the requested dataset sources.
+    Returns (list_of_(row, enrich_fn) tuples, dict of source->count).
+    """
+    all_rows = []
+    total_per_source = {}
+
+    bitext_sources = []
+    load_massive = False
+
+    if sources == "all":
+        bitext_sources = list(DATASET_CONFIGS.items())
+        load_massive = True
+    elif sources == "massive":
+        load_massive = True
+    elif sources in DATASET_CONFIGS:
+        bitext_sources = [(sources, DATASET_CONFIGS[sources])]
+    else:
+        raise ValueError(
+            f"Unknown source '{sources}'. "
+            f"Choose from: {list(DATASET_CONFIGS.keys()) + ['massive', 'all']}"
+        )
+
+    # Load Bitext English datasets
+    for name, cfg in bitext_sources:
+        print(f"\n  Downloading: {cfg['id']}")
+        print(f"  License    : {cfg['license']} | Language: English")
+        t0 = time.time()
+        ds = load_dataset(cfg["id"], split="train")
+        elapsed = time.time() - t0
+        print(f"  Downloaded : {len(ds):,} rows in {elapsed:.1f}s")
+        total_per_source[name] = len(ds)
+        for row in ds:
+            all_rows.append((row, cfg["domain"], cfg["license"], "bitext", "en", "English"))
+
+    # Load multilingual MASSIVE datasets
+    if load_massive:
+        for lang_code, lang_cfg in MASSIVE_LANGUAGES.items():
+            lang_name = lang_cfg["name"]
+            mteb_config = lang_cfg["mteb_config"]
+            print(f"\n  Downloading: mteb/amazon_massive_intent [{lang_code} - {lang_name}]")
+            print(f"  License    : Apache-2.0 | Language: {lang_name}")
+            t0 = time.time()
+            ds = load_dataset("mteb/amazon_massive_intent", mteb_config, split="train")
+            elapsed = time.time() - t0
+            print(f"  Downloaded : {len(ds):,} rows in {elapsed:.1f}s")
+            total_per_source[f"massive_{lang_code}"] = len(ds)
+            for row in ds:
+                all_rows.append((row, "multilingual_intent", "Apache-2.0", "massive", lang_code, lang_name))
+
+    return all_rows, total_per_source
+
+
+def main(limit: int = None, delay: float = 0.0, sources: str = "all"):
+    print("=" * 65)
+    print("CustomerCore Multi-Language Data Loader")
+    print("Sources: Bitext SaaS + Banking (EN) + Amazon MASSIVE (DE/FR/ES)")
+    print("License: CDLA-Sharing-1.0 + Apache-2.0 | Legal in EU/Germany")
+    print("=" * 65)
+
+    # ── 1. Download datasets ──────────────────────────────────
+    print("\n[1/3] Downloading datasets from Hugging Face...")
+    t_start = time.time()
+    all_rows, source_counts = load_sources(sources)
+
+    print(f"\n  Source breakdown:")
+    for src, count in source_counts.items():
+        print(f"    {src:18s}: {count:,} rows")
+    total = len(all_rows)
+    print(f"  {'TOTAL':18s}: {total:,} rows")
+
+    if limit:
+        random.shuffle(all_rows)
+        all_rows = all_rows[:limit]
+        print(f"\n  Limited to {len(all_rows):,} rows for this run")
+
+    # ── 2. Connect to Redpanda ────────────────────────────────
+    print(f"\n[2/3] Connecting to Redpanda at {BROKER}...")
+    producer = Producer({
+        "bootstrap.servers": BROKER,
+        "queue.buffering.max.messages": 10000,
+        "batch.num.messages": 500,
+    })
+    print("      Connected.")
+
+    # ── 3. Publish with progress bar ──────────────────────────
+    print(f"\n[3/3] Publishing {len(all_rows):,} events to '{TOPIC}'...")
+    print(f"      Estimated time: {len(all_rows) * 0.001:.0f}–{len(all_rows) * 0.003:.0f} seconds")
+
+    failed = 0
+    lang_counts: dict[str, int] = {}
+    t0 = time.time()
+    with tqdm(
+        total=len(all_rows),
+        unit="msg",
+        bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]"
+    ) as pbar:
+        # Each tuple: (row, domain, license_str, source_type, lang_code, lang_name)
+        for row, domain, license_str, source_type, lang_code, lang_name in all_rows:
+            if source_type == "bitext":
+                event = enrich(row, domain, license_str)
+            else:  # massive
+                event = enrich_massive(row, lang_code, lang_name)
+
+            lang_counts[lang_code] = lang_counts.get(lang_code, 0) + 1
+
+            producer.produce(
+                topic=TOPIC,
+                key=event["ticket_id"].encode(),
+                value=json.dumps(event).encode(),
+                callback=lambda err, msg: None if not err else None,
+            )
+            producer.poll(0)
+            pbar.update(1)
+            if delay:
+                time.sleep(delay)
+
+    producer.flush()
+    elapsed = time.time() - t0
+    sent = len(all_rows) - failed
+
+    print(f"\n{'=' * 65}")
+    print(f"  Published   : {sent:,} events")
+    print(f"  Failed      : {failed}")
+    print(f"  Duration    : {elapsed:.1f}s  ({len(all_rows)/elapsed:.0f} msg/s)")
+    print(f"  Topic       : {TOPIC}")
+    print(f"  Sources     : {sources}")
+    print(f"  Language breakdown:")
+    for lang, count in sorted(lang_counts.items()):
+        print(f"    {lang}: {count:,} rows")
+    print(f"{'=' * 65}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CustomerCore Multi-Language HuggingFace loader")
+    parser.add_argument(
+        "--limit", type=int, default=None,
+        help="Max rows to load (default: all ~87k combined)"
+    )
+    parser.add_argument(
+        "--delay", type=float, default=0.0,
+        help="Delay between messages in seconds"
+    )
+    parser.add_argument(
+        "--sources",
+        choices=["all", "customer", "banking", "massive"],
+        default="all",
+        help="Which dataset sources to load (default: all = EN+DE+FR+ES)"
+    )
+    args = parser.parse_args()
+    main(limit=args.limit, delay=args.delay, sources=args.sources)
+

src/streaming/minio_setup.py

@@ -0,0 +1,66 @@
+"""
+src/streaming/minio_setup.py
+
+Creates the MinIO bucket and folder structure for the lakehouse.
+Run once before starting any pipeline.
+
+Run: python -m src.streaming.minio_setup
+"""
+
+import boto3
+from botocore.client import Config
+from botocore.exceptions import ClientError
+
+# ── MinIO connection ──────────────────────────────────────────
+MINIO_ENDPOINT = "http://localhost:9000"
+MINIO_ACCESS_KEY = "minioadmin"
+MINIO_SECRET_KEY = "minioadmin"
+BUCKET = "customercore-lake"
+
+# ── Bronze/Silver/Gold prefix structure ───────────────────────
+PREFIXES = [
+    "bronze/tickets/",
+    "bronze/billing/",
+    "bronze/product/",
+    "bronze/incidents/",
+    "silver/tickets/",
+    "silver/billing/",
+    "silver/product/",
+    "silver/incidents/",
+    "gold/",
+]
+
+
+def get_client():
+    return boto3.client(
+        "s3",
+        endpoint_url=MINIO_ENDPOINT,
+        aws_access_key_id=MINIO_ACCESS_KEY,
+        aws_secret_access_key=MINIO_SECRET_KEY,
+        config=Config(signature_version="s3v4"),
+        region_name="us-east-1",
+    )
+
+
+def setup():
+    client = get_client()
+
+    # Create bucket if it doesn't exist
+    try:
+        client.head_bucket(Bucket=BUCKET)
+        print(f"[OK] Bucket '{BUCKET}' already exists")
+    except ClientError:
+        client.create_bucket(Bucket=BUCKET)
+        print(f"[CREATED] Bucket '{BUCKET}'")
+
+    # Create folder structure by writing empty marker objects
+    for prefix in PREFIXES:
+        client.put_object(Bucket=BUCKET, Key=prefix, Body=b"")
+        print(f"  [OK] {BUCKET}/{prefix}")
+
+    print(f"\nLakehouse structure ready at MinIO: {MINIO_ENDPOINT}")
+    print(f"Browse at: http://localhost:9001 (user: minioadmin / pass: minioadmin)")
+
+
+if __name__ == "__main__":
+    setup()