// services/rag
Retrieval pipelines your users can trust
We build retrieval-augmented generation systems that turn your documents, tickets and databases into accurate, cited answers — with hybrid search, re-ranking and continuous evals.
// the_problem
Grounded answers from your knowledge
Out-of-the-box RAG demos well and fails quietly. Naive chunking, pure vector search and missing evals produce answers that are confidently wrong. Production RAG is a measurement problem as much as a retrieval problem.
// deliverables
What we hand over
Ingestion & indexing pipeline
Robust parsing of PDFs, wikis, tickets and structured data with smart chunking, metadata extraction and incremental updates.
Hybrid retrieval layer
Vector, keyword and metadata search combined with re-ranking, tuned against your real queries — not benchmarks alone.
Answer layer with citations
LLM synthesis with inline citations, refusal when evidence is thin, and feedback loops feeding retrieval evals.
- Hybrid search (vector + keyword + graph)
- Document ingestion and chunking pipelines
- Re-ranking and query rewriting
- Citations and source attribution
- Freshness: incremental sync and re-indexing
- Retrieval quality evals and dashboards
// faq
Questions we hear a lot
Vector database or Postgres with pgvector?
For most workloads pgvector wins on operational simplicity. We move to dedicated engines like Qdrant when scale, filtering complexity or latency budgets demand it.
How do you measure RAG quality?
Golden datasets built from your real queries, scored on retrieval hit-rate, citation faithfulness and answer accuracy — tracked continuously in CI so regressions ship nowhere.
Can RAG respect document permissions?
Yes. We enforce access control at query time by filtering retrieval results through the requesting user's permissions, so users only ever see what they're allowed to see.