◆ Service
RAG & retrieval
We build retrieval-augmented generation that cites its sources and refuses to hallucinate. Our pipelines combine semantic search with structured lookups over pgvector, Neo4j and BM25, backed by ingestion pipelines built to scale with your data.
What you get
- Hybrid retrieval: semantic embeddings, structured lookups and BM25
- Strict citation contracts that eliminate "not available" hallucinations
- Vector stores on pgvector, plus Neo4j knowledge graphs where relationships matter
- Config-driven, on-demand ingestion pipelines
- Sub-200ms retrieval latency with caching
- Evaluation and observability so answer quality is measured, not guessed
- pgvector
- Neo4j
- BM25
- Hybrid retrieval
- Knowledge graphs
Frequently asked
How do you stop RAG from hallucinating?
We enforce a citation contract so the model can only answer from retrieved, grounded context, and we return sources with every answer. If the context does not contain the answer, the system says so instead of inventing one.
Vector search or knowledge graph?
Both, when it helps. Semantic vector search finds relevant passages; a knowledge graph captures entity relationships a pure vector store misses. We have shipped dual-module systems that lifted query accuracy by 40%.
Can you improve our existing RAG?
Yes. We audit retrieval quality, fix chunking and ingestion, add hybrid retrieval and evaluation, and cut latency. The biggest wins are usually in retrieval, not the model.
Have a rag & retrieval project?
Production-grade, owned end to end. Usually a reply within a day.