RAG assistants on your data
AI assistants that answer from your own documents, with citations you can verify.
We build retrieval-augmented generation (RAG) assistants grounded in your private knowledge: policies, contracts, tickets, wikis and product docs. The model retrieves the relevant passages first, then answers with sources cited, so staff get accurate, traceable answers instead of confident guesses. Permission-aware retrieval means each user only sees what they are cleared to see.
Included in this service
Grounded retrieval
Vector and keyword (hybrid) search over your documents, tuned with chunking and re-ranking for relevance.
Cited answers
Every response links back to the source passage so users can check it, with abstention when the answer is not in the data.
Permission-aware access
Retrieval respects your existing access controls, so the assistant never surfaces documents a user cannot see.
Fresh knowledge
Automated ingestion keeps the index current as documents change, no model retraining required.
What you walk away with
- Deployed RAG assistant with a chat interface
- Indexing and ingestion pipeline for your sources
- Retrieval evaluation report (recall, precision, groundedness)
- Source citation and access-control configuration
- Integration with your data sources
- Cost and latency tuning
A clear path from problem to outcome
Frame and qualify
We pin down the business outcome, the data you already hold and the constraints (latency, budget, privacy, residency). We agree success metrics up front, an offline accuracy or quality target plus a business KPI, so we build something measurable, not a demo.
Prototype on your data
We build a working proof of concept against a representative slice of your real data, not a public dataset. You see honest numbers early: retrieval quality, model accuracy, cost per request and failure cases, so the go or no-go decision is evidence based.
Engineer for production
We harden the prototype into a reliable system: data and feature pipelines, evaluation suites, access controls, observability and CI/CD. We integrate with your stack and put guardrails and human review where the cost of an error is real.
Deploy, monitor and improve
We ship to production, instrument it and watch for drift, regressions and cost creep. You get clear documentation, a retraining or re-indexing routine and an evaluation baseline so quality holds up and you can improve it over time.
Frequently asked questions
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