Offering · Enterprise Knowledge System

Your documents, searchable. Citable. Governed.

Production-grade RAG and knowledge systems — source integrations, retrieval with citations, role-based access, audit logging, and an evaluation harness so you know it's working. Not a demo.

Who It's For

If your team has hit one of these walls.

Accuracy

The answers are wrong — or unverifiable.

Retrieval misses, hallucinations leak through, and there's no way for users to check the source. No evaluation in place to even measure it.

Permissions

Every user sees everything.

The knowledge system works — until legal asks how role-based access, document-level ACLs, and audit logging are enforced. They aren't.

Integration

The docs live in five places.

SharePoint, Confluence, Drive, S3, internal databases. None of them talk to each other, and the ingestion pipeline is a fragile script.

Deliverables

What you get.

A production-grade RAG system, integrated, evaluated, governed.

01

Source Integrations

Connectors for SharePoint, Confluence, Google Drive, S3, and internal databases. Incremental sync, change capture, and source-of-truth resolution.

02

Retrieval Pipeline

Chunking, embedding, hybrid search (lexical + vector), reranking, and query rewriting. Tuned for your domain, not a starter template.

03

Citations & Provenance

Every answer cites the source. Users click through to the exact passage and document version that produced the answer.

04

Role-Based Access

Document-level ACLs flow through retrieval — users only see what they're authorized to see, enforced at query time.

05

Audit Logging

Every query, every retrieved chunk, every answer logged. Searchable by user, document, time — for compliance and debugging.

06

Evaluation Harness

Golden dataset, automated evals, drift detection. You know when retrieval quality moves — before users tell you.

How It's Built

Production stack — no toys, no surprises.

We use the same patterns we run in production ourselves. Each piece is replaceable — you own the system at the end, not the vendor.

  • LLM-agnostic — Anthropic, OpenAI, or self-hosted.
  • Vector store of your choice — Postgres, Qdrant, Weaviate.
  • Observability via Langfuse for trace, cost, and quality.
  • Deployed to your cloud — no third-party data path.
Engagement at a Glance
Team2–3 senior engineers
Timeline~6 weeks (scoped)
ModelFixed scope
DeployYour cloud · your stack
HandoverCode · runbook · evals · training
PricingScoped — contact us

RAG that survives an audit.

Tell us what your team is trying to make searchable. We'll come back with a scoped plan — sources, evals, timelines, and what production-grade actually means for your data.