A knowledge system your team can actually trust.
Build · 4–8 weeks · fixed priceYour documents, contracts, policies and tickets become answerable — sourced, versioned, and auditable. Every answer cites where it came from. Unsourced claims are refused, not faked.
Knowledge is trapped — and a chatbot over it isn't enough.
Policies, contracts, manuals, ticket history — the answer exists, but no one can find it under deadline.
Naive RAG produces confident-sounding text that doesn't actually appear in your sources.
Without evaluation, retrieval and model changes silently degrade answers over time.
Retrieval first. Generation second. Citation always.
- Ingestion
- Structure-aware chunking, metadata, document-permission inheritance
- Retrieval
- Hybrid lexical + vector search, query rewriting, reranking
- Generation
- Plan-then-answer with explicit source binding and refusal on low support
- Citation
- Every claim links back to the exact passage; users can verify in one click
- Evaluation
- Golden Q/A suite, grounded-claim checks, citation accuracy scoring
- Operations
- Re-ingest on doc change, drift alerts, monthly quality reviews
A system that says “I don't know” when it doesn't.
The bar is not “a chatbot that answers everything.” The bar is a system that answers what it can cite, refuses what it can't, and gets re-evaluated as your corpus grows.
Often paired with LLM features or an agent.
LLM Integration
The retrieval system most often powers LLM features in your existing tools.
/services/custom-ai-agentCustom AI Agent
Agents that need to reason over your documents call into the same retrieval layer.
/services/ai-auditAI Audit
Knowledge work usually surfaces in the audit. The corpus and the questions are scoped there.
Make your sources answerable — without losing the source.
We start with an audit to scope the corpus, the questions, and the bar. Then we build, evaluate and hand over.