FDA Compliance Copilot: Compliance RAG Platform

A freelance engagement building an end-to-end Retrieval-Augmented Generation (RAG) platform that powers FDA-style compliance and quality-management workflows over large volumes of unstructured documentation. This is the project where disciplined engineering meets production Generative AI — accuracy, latency, cost efficiency, and observability all treated as first-class requirements.

The Problem

Compliance and quality teams sit on top of sprawling, unstructured documentation — SOPs, audit records, deviation reports, regulatory guidance. Finding the right answer (and being able to cite it) is slow and error-prone, and generic LLMs hallucinate when asked domain-specific questions without grounding.

RAG Architecture

Ingestion pipeline: Built document ingestion spanning parsing, semantic chunking, embedding generation, and vector indexing.

Embeddings + Vector Store: OpenAI embeddings indexed in Pinecone for fast, relevant semantic retrieval.

Query enrichment: Used Gemini for query normalization and contextual enrichment so user intent maps cleanly onto the indexed knowledge.

Grounded generation: Retrieval-tuned prompting with contextual memory to keep answers factual and citation-backed.

AI Products Built

CAPA Copilot: An AI assistant that generates structured Corrective and Preventive Action compliance reports grounded in retrieved domain knowledge — turning hours of manual report drafting into a guided, evidence-backed workflow.

Conversational Search Copilot: A grounded, citation-backed Q&A experience over the document corpus, so every answer traces back to its source.

Engineering for Reliability

Service layer: Exposed AI workflows as scalable Python and FastAPI services.

Accuracy: Applied prompt engineering, retrieval tuning, and contextual memory to reduce hallucinations and improve factual grounding.

Observability: Established logging and evaluation to measure retrieval quality, response relevance, and reliability.

Cost & latency: Optimized retrieval latency and token/cost efficiency as explicit, measured targets — not afterthoughts.

Outcome

A production-grade RAG platform that lets compliance and quality teams query unstructured documentation conversationally and generate grounded, citation-backed reports — demonstrating the full lifecycle of building reliable Generative AI products on top of OpenAI, Gemini, Pinecone, Python, and FastAPI.