# Qualora Documentation **Platform Version:** 1.0 (Early Access) **Architecture:** Multi-Tier LLM Cascade & RAG Retrieval Welcome to the Qualora Enterprise Quality Auditing Platform documentation. This technical guide outlines the core functional domains, inference architecture, and security protocols governing the Qualora ecosystem. --- ## 1. Executive Overview Qualora is a GenAI-native quality auditing copilot engineered to automate the evaluation of customer support interactions at scale. By synthesizing advanced Retrieval-Augmented Generation (RAG) with a fault-tolerant LLM cascade, Qualora provides objective, deterministic scoring across critical dimensions including agent empathy, regulatory compliance, and operational efficiency. ## 2. Core Audit Engine The Audit Engine acts as the primary ingestion and inference layer, natively supporting deterministic pipelines for both voice and text modalities. ### 2.1 Voice Audits - **Ingestion**: The platform accepts raw audio payloads (MP3, WAV, M4A, OGG) up to 50MB, or direct browser-based streams via the integrated MediaRecorder API. - **Transcription Cascade (Tier 1)**: HuggingFace (HF) Space nodes executing localized WhisperX models, yielding high-fidelity transcriptions with speaker diarization and prosody metadata. - **Transcription Cascade (Tier 2)**: High-availability fallback routing through API chains, leveraging ElevenLabs Scribe, Deepgram Nova-2, or Groq Whisper-large-v3. - **Evaluation**: Normalized transcripts are routed asynchronously to the LLM cascade for multi-dimensional scoring against the tenant's knowledge base. ### 2.2 Text and Chat Audits - **Ingestion**: Supports unstructured raw text insertion or structured document parsing (PDF, CSV, JSON, TXT). - **Sanitization**: Payloads undergo pre-processing and sanitization before being audited against organization-specific compliance schemas. ### 2.3 LLM Inference Cascade To ensure strict zero-downtime availability, Qualora routes all evaluation queries through a resilient fallback hierarchy: 1. **Primary Tier**: Google Gemini 2.0 Flash (Optimized for high-context, low-latency reasoning). 2. **Secondary Tier**: Llama 3.3 via OpenRouter (High-performance secondary logic). 3. **Tertiary Tier**: Llama 3.3 via Groq (Ultra-fast inference recovery layer). 4. **Resort Tier**: HuggingFace Inference Endpoints (Emergency failover). --- ## 3. RAG Knowledge Base (KB) The Knowledge Base is the organizational "Source of Truth," enabling supervisors to map arbitrary policy documents directly into the AI Judge's scoring logic. - **Atomic Vectorization (Primary Store)**: MongoDB Atlas Vector Search for secure, cloud-native semantic retrieval. - **Atomic Vectorization (Caching Store)**: ChromaDB for localized, high-throughput persistent caching. - **Retrieval Pipeline**: Upon audit execution, the RAG engine retrieves the highest-relevance policy clauses, appending them to the system prompt to constrain and calibrate the model's evaluation. --- ## 4. Human-In-The-Loop (HITL) Workflow Qualora strictly enforces a human-centric auditing philosophy, ensuring automated outputs do not execute autonomously in sensitive HR environments. - **Supervisor Overrides**: Administrators and Supervisors possess unilateral rights to review AI-generated reports, marking them as Approved, Flagged, or Rejected. - **State Reversibility**: Destructive or status-modifying commits carry a 10-second grace window, mitigating accidental state changes. - **Contextual Annotations**: Supervisors may append manual coaching notes directly to the immutable audit trail to supplement the AI's behavioral nudges. --- ## 5. Agent Telemetry and Performance Agent evaluation is quantified through continuous, multi-dimensional tracking: - **Quality Matrix**: Interactions are scored against five core dimensions: Language Proficiency, Cognitive Empathy, Efficiency, Bias Reduction, and Active Listening. - **F1 Trend Analysis**: The dashboard visualizes a rolling time-series graph of an agent's recent 50 audits, highlighting standard deviations in performance. - **Risk Stratification**: The platform algorithms isolate agents demonstrating recurring critical (Red) or cautionary (Amber) compliance flags, optimizing targeted coaching prioritization. --- ## 6. Real-Time Compliance Alerts The system implements a continuous monitoring daemon to trigger localized alerts upon detecting: 1. **Critical Breach**: Immediate detection of Red-Risk compliance violations during an interaction. 2. **Performance Degradation**: An agent's rolling F1 score dropping below the 0.6 threshold. 3. **Systematic Failure**: Compounding failures across generalized matrix criteria. - **Integration**: Enterprise deployment allows dynamic dispatching of these alerts via Webhooks to Slack or Discord channels. --- ## 7. Security and Tenant Isolation Security represents a foundational pillar of the Qualora architecture: - **Authentication Vectors**: Sessions are exclusively governed by HTTP-Only JWT cookies, nullifying exposure to Cross-Site Scripting (XSS) payload extraction. - **CSRF Mitigation**: All state-mutating requests (POST/PUT/DELETE) strictly require header-injected X-CSRF-Token validation. - **Data Sovereignty**: Robust database-level isolation guarantees that tenants can solely query and mutate data tethered strictly to their internal Organizational ID. --- ## 8. Troubleshooting Logistics - **System Diagnostics**: Utilize the "System Status" indicator in the application footer for real-time ping tracking of API targets, database connectivity, and average response latencies. - **Inference Fast Track**: Should standard inference pipelines experience degradation, users may manually invoke the "Fast Track" directive, forcefully shifting the request to the highest-speed node available. - **Session Lifecycles**: Strict TTL (Time-To-Live) constraints govern active sessions; expired tokens will execute a secure, automatic redirect to the authentication gateway.