Docs/README.md

/docs — Enterprise Knowledge Copilot

Living documentation for the Enterprise Knowledge Copilot system. Read in order before writing code. Update when architecture changes.

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Document Map

File	Contents	Read When
01_problem_and_architecture.md	Problem statement, objectives, system overview, all 5 areas, agentic design, user roles, design principles	First. Always read this first.
02_rag_pipeline_and_validation.md	Ingestion pipeline, chunking, T1/T2/T3 retrieval, dual index, GLiNER PII, Generator+Critic validation, Knowledge Loop, Dependency Tracker	Building ingestion, retrieval, or validation components
03_analytics_and_intelligence.md	Query classification, interaction log schema, retrieval feedback loop, NL analytics, health dashboard, proactive agent, silo detector, Areas 4 & 5 planned specs	Building analytics, dashboards, or Area 3 agents
04_integrations_and_tech_stack.md	Notion, Confluence, GitHub integration specs with full code; RBAC enforcement; change detection; tech stack; local dev setup; env vars	Building any integration or setting up development environment
05_market_strategy_and_gtm.md	Target customer, tool strategy, competitor analysis, USPs, country-by-country market analysis, GTM sequencing	Product, positioning, or expansion decisions
anomaly-and-forecasting/	Area 4 implementation docs — data layer, detection algorithms, API reference, Celery scheduling	Building or extending anomaly detection, forecasting, or the Anomalies frontend tab

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The Five Focus Areas at a Glance

Area 1 — Hybrid RAG System          [Core — Implemented]
Area 2 — Data Pipelines & Validation [Core — Implemented]
Area 3 — Analytics & NL Intelligence [Core — Implemented]
Area 4 — Anomaly & Forecasting       [Implemented — branch: anomaly-and-forecasting]
Area 5 — Knowledge Graph             [Implemented]

All five areas are one system — not five products. See 01_problem_and_architecture.md for the interaction map.

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Quick Reference

Primary Tool Integrations

• Notion → 04_integrations_and_tech_stack.md#2-notion-integration
• Confluence → 04_integrations_and_tech_stack.md#3-confluence-integration
• GitHub → 04_integrations_and_tech_stack.md#4-github-integration

Key Architectural Decisions

• One agent, one tool (never multi-tool agents)
• GLiNER runs locally — zero data egress, always
• Generator and Critic are always separate agents
• Semantic chunking only — no fixed-size splits
• Every interaction is a logged data point feeding Area 3

Launch Markets

1. 🇮🇳 India — DPDP compliance moat, zero competition, perfect tool stack
2. 🇸🇬 Singapore — SEA regional HQ beachhead
3. 🇦🇺 Australia — English-language, Privacy Act reform, same tool stack