Overview
RAG lets LLMs answer questions grounded in your documents — without hallucinating facts from training data. We build RAG pipelines for enterprise knowledge bases, classified document corpora, legal and medical libraries, and product catalogs. Retrieval accuracy is measured and benchmarked. Hallucination is not acceptable in production.
The Problem
Basic RAG implementations work in demos and break in production. Fixed chunk sizes miss context. Naive embedding similarity returns irrelevant results. Response latency is too high. The model hallucinates when retrieved context is insufficient. These problems are solvable with good engineering — but require careful benchmarking and iteration.
Our Approach
We evaluate multiple chunking strategies, embedding models, and retrieval approaches (dense, sparse, hybrid) against your specific document corpus. We benchmark retrieval accuracy before connecting an LLM. We add re-ranking, query reformulation, and fallback handling for low-confidence retrievals. pgvector for on-premise vector storage. MLflow to track retrieval experiments.
Deliverables
- Document ingestion pipeline
- Embedding and vector storage (pgvector)
- Retrieval benchmarking
- Re-ranking and query optimisation
- LLM grounding and citation
- On-premise or cloud deployment