Answers grounded in your data, with sources
Hybrid retrieval combines vector search, keyword search and structured filters so answers come from the right documents. We build it on PostgreSQL with pgvector where that fits.
How we approach it
Vector search alone misses exact terms, codes and names; keyword search alone misses meaning. Hybrid retrieval merges both, re-ranks the results and passes the best passages to the model with their sources.
Where relationships matter, we add a lightweight knowledge graph in the same database, so questions about people, products and dependencies can follow links instead of guessing.
Retrieval quality decides answer quality
Most hallucination in enterprise assistants is a retrieval failure. We measure retrieval separately from generation.
- Fresh data via incremental re-indexing.
- Clear behaviour when the answer is not in the data.
- Evaluation sets built from your real questions.
Step by step
Ingest
Parse, chunk and enrich documents with metadata.
Retrieve
Vector plus keyword plus filters.
Re-rank
Order candidates by relevance to the question.
Answer with sources
Citations the user can open.
What you receive
Hybrid retrieval
pgvector with full-text search and structured filters.
Access control
Retrieval respects who may see what.
Citations
Every answer links back to the passages used.
Retrieval evaluation
Measure recall and precision on real questions.
Common questions
Do we need a separate vector database?
Often not. PostgreSQL with pgvector handles many workloads and keeps data in one place. We recommend a dedicated store when scale or latency requires it.
How do you prevent leaking restricted documents?
Permissions are enforced at retrieval time, not only in the prompt.
Can it handle PDFs and scans?
Yes, with parsing and OCR as part of the ingestion pipeline.
Scope your agent system with an engineer
Bring the process you want to automate. We will talk through the architecture, the risks and a realistic first release.