Answers grounded in your own knowledge
We build retrieval-augmented generation systems that answer from your documents, tickets and databases — with citations, access control and an evaluation harness that keeps accuracy honest.
Technology we build with
Retrieval that goes beyond cosine similarity
Naive RAG retrieves the wrong chunks and quietly hallucinates. We engineer the whole pipeline — chunking, hybrid retrieval, reranking and grounding checks — so answers cite their sources and accuracy moves from roughly seventy percent to the high nineties.
- Hybrid retrieval combining semantic vectors with keyword and metadata filters
- Rerankers and grounding checks that reject answers the sources don't support
- Inline citations back to the exact passage, so every claim is verifiable
- Permission-aware retrieval that respects each user's access at query time
A retrieval pipeline tuned for accuracy
Each stage engineered and measured so grounded answers stay trustworthy as your corpus grows.
Hybrid retrieval
Dense vectors, sparse keywords and metadata filters combined to surface the genuinely relevant context.
Citations & grounding
Every answer links to the source passage, and ungrounded claims are caught before they reach a user.
Permission-aware access
Row- and document-level access control applied at retrieval time so users only see what they should.
Reranking layer
Cross-encoder rerankers that reorder candidates by true relevance, lifting answer precision sharply.
Freshness sync
Incremental indexing keeps the knowledge base current as your source systems change.
Evaluation harness
Continuous scoring of retrieval and answer quality so regressions are caught before release.
Grounded answers across every knowledge source
The retrieval and grounding capabilities we assemble for your domain.
Evaluation-driven
Every build ships with an evaluation suite, so quality is measured rather than asserted.
Deployed your way
Your cloud account, VPC or on-premise — including open-weight models where data cannot leave.
Source-code handover
You receive the code and the documentation. No lock-in to us to keep it running.
Human in the loop
Approval gates and review queues wherever an automated mistake would be costly.
Common questions
By engineering the whole pipeline rather than just the prompt — considered chunking, hybrid retrieval, cross-encoder reranking and grounding checks — and measuring each stage separately with an evaluation set built from your own questions, so improvements are demonstrated on your data rather than promised in advance.
Ground your AI in the knowledge you own
We'll stand up a cited, permission-aware assistant over your data and prove its accuracy with evals.