About This Architecture

TRAI's unified AI/ML layer implements a C4 system context and layered architecture for regulated government operations, integrating document processing, vector retrieval, and LLM inference with strict governance controls. Data flows from external sources (DMS, SFTP, APIs, portals) through ingestion and validation layers into a semantic knowledge layer powered by Qdrant vector DB, OpenSearch lexical search, and Llama 3.1 70B via vLLM. The AI Orchestrator routes requests across specialized services—embedding (BAAI/bge-m3), reranking (bge-reranker-v2-m3), output validation, and guardrails—ensuring compliance, audit trails, and human approval workflows. This architecture demonstrates enterprise-grade AI governance, role-based access control, and hallucination prevention critical for public-sector AI systems. Fork and customize this diagram on Diagrams.so to adapt the layered design, component interactions, and security posture for your regulated AI platform.

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How do you design an enterprise AI/ML platform for regulated government operations with document processing, vector retrieval, LLM inference, and strict governance controls?

TRAI's architecture uses a layered approach: access/ingestion layer with security checks and OCR, enterprise data layer with DMS and vector/lexical stores, semantic knowledge layer with Qdrant and OpenSearch, and an AI Orchestrator routing requests through embedding, reranking, validation, and guardrail services. This ensures compliance, audit trails, and hallucination prevention for public-sector