Enterprise RAG AI: Private Doc Intelligence A fast-growing B2B software firm needed a secure, enterprise-grade Retrieval-Augmented Generation (RAG) system to query thousands of confidential internal documents (legal contracts, technical architecture specs, Notion pages, and employee SOPs) in real time without third-party data leakage.

Key Challenges:

High hallucination risk and irrelevant context retrieval using standard keyword search.

Managing multi-format files (scanned PDFs, tables, markdown) with strict access control based on user hierarchy.

Technical Implementation & Solution: • Engineered an automated ingestion pipeline in Python with LlamaIndex and LangChain for semantic chunking and metadata preservation. • Integrated a hybrid vector search architecture pairing dense embeddings (OpenAI text-embedding-3) with sparse BM25 indexing in Qdrant. • Added a cross-encoder re-ranking layer (Cohere Re-ranker) to prune low-relevance chunks, reducing API token costs by 40%. • Built robust Role-Based Access Control (RBAC) via FastAPI endpoints, ensuring departmental data isolation.

Results & Impact: • Dropped internal information retrieval time from 20 minutes to under 4 seconds. • Attained 98% factual precision with traceable in-line page citations. • Full on-prem Docker deployment ensuring 100% private data security.

I'd test revoking a user's document access mid-conversation. In your RBAC flow, do cached retrieval results get rechecked against current permissions before they're sent to the reranker or model?

I’m building Paddy, a platform for finding verified rental homes in Ghana.

Every listing is reviewed before it goes live. The problem: a rejected landlord had no idea what went wrong, so they’d either give up or resubmit the same thing.

So now, before a reviewer can reject a listing, a dialog asks them for the reason. The landlord sees it on their dashboard and knows exactly what to fix.

Built with Next.js on a Django REST API. Code in the comments.