KnowledgeKIT
RAG and enterprise search, without the plumbing. Ingest documents, wikis, and tickets. Ship semantic search and Q&A over your private data.
KnowledgeKIT is a production retrieval stack for teams that want RAG that actually works — grounded answers with citations, respect for source permissions, and evaluation baked in. It's aimed at orgs with years of content in Confluence, Drive, Notion, SharePoint, Zendesk, and S3 that want a single answer surface without rebuilding their information architecture.
Connectors perform incremental sync with ACL mirroring, feeding layout-aware parsers (Unstructured, custom table and slide extractors) that produce typed chunks. Embeddings land in pgvector or Qdrant alongside a BM25 lexical index; queries run hybrid retrieval with Cohere Rerank on top. Grounded answers are generated with citation enforcement, and every release runs against an eval harness of real user queries before it ships.
On day 1 you have connectors syncing your top sources, hybrid search exposed via API and a basic UI, and grounded Q&A returning citations.
By week 3 chunking and reranking are tuned to your content mix, answer prompts reflect your voice and escalation rules, and the eval suite tracks regression against a curated query set from real usage.
Build it from scratch, or start a week ahead
- Six to nine months building the orchestration, memory and recovery before a first real result.
- An evaluation harness you write from scratch — then argue about.
- A team learning your edge cases live, in production.
- A black-box vendor you can't inspect, tune or move off.
- A working KnowledgeKIT on your data in week one — the hard parts already solved.
- Evals seeded on day one and graded against your real workflows.
- Senior owners with full traces and dashboards from the first deploy.
- You own the prompts, the weights, the traces and the outcomes.
Ships with the hard parts solved
Connectors & ingestion
Drive, Notion, Confluence, SharePoint, S3, Zendesk — with incremental sync.
Parsing & chunking
Layout-aware extraction for PDFs, slides, and tables.
Hybrid search
Dense + lexical retrieval with reranking and ACL filtering.
Grounded answers
Citations, confidence, and follow-up questions for every response.
Where teams deploy it
Fine-tuned on your data and shaped to the workflow it lands in — these are the deployments we see most.
Kick-off to production in three weeks
A fixed scope and a visible finish line — you see it working before it's load-bearing.
Week 1 — Integration
Connectors and permissions mirrored from your source systems.
Week 2 — Fine-tune
Chunking, reranking, and answer prompts tuned on real queries.
Week 3 — Ship
Embedded search UI, API, and eval suite live in your org.
RAG looks simple in a demo and breaks in production — on permissions, on tables, on stale content, on hallucinations. KnowledgeKIT is the accumulated answer to those failure modes, so you ship a system that holds up under enterprise scrutiny.
“They understand our needs quickly and are a delight to work with.”
KnowledgeKIT, live on your stack in a week
One call to scope it, a senior team on it from day one — and a fixed scope agreed before we start.