RAG systems that cite their sources or say no.

What we build

• Internal "chat with your data" assistants grounded in your documents, with citations that link back to the source.
• Document ingestion and chunking pipelines tuned to your corpus — contracts, filings, tickets, wikis, not lorem ipsum.
• Hybrid retrieval with reranking: vector search where it helps, classical IR where it wins, measured against baselines rather than assumed.
• Eval sets that run in CI on every prompt, retriever, or model change — retrievable, adjacent-but-wrong, and adversarial questions.
• Rescues of RAG systems your team has stopped trusting.

The two rules every build ships with

Cite or refuse: every answer either quotes sources with attribution or says "I don't know" — no graceful hallucination. And the eval harness comes first: the failure modes become tests before any prompt is written, so regressions surface in CI, not in production.

Proof, not promises

We rebuilt an internal Q&A bot the support team had stopped trusting: 64% to 91% answer accuracy in three weeks, with an eval set the client still runs on every prompt change. Our document-retrieval pipeline is benchmarked against classical IR baselines, and the same posture runs in production at a financial-compliance software company.

Stack

• Claude API
• OpenAI API
• LangChain
• LlamaIndex
• ChromaDB
• Pinecone
• pgvector
• Cross-encoder reranking
• FastAPI
• Postgres

RAG is one part of our AI automations & agents practice — and retrieval is only as good as the data infrastructure underneath it.