The problem
ChatGPT is impressive right up until you ask it something about your business — your prices, your processes, your ten years of project files — and it either shrugs or invents an answer. Meanwhile the knowledge that runs your company is scattered across PDFs, spreadsheets, old emails, and one senior employee's head. And the obvious "fix" — pasting your private documents into a public chatbot — is exactly the data-handling mistake you shouldn't make.
What we do
We build AI systems grounded in your data — the discipline is called RAG, retrieval-augmented generation, and the plain-English version is: the AI looks it up before it opens its mouth. Your documents and records get organized into a retrieval system built for your content; when someone asks a question, the AI finds the actual relevant passages from your material and answers from them — and it can show its sources, so "where did that come from?" always has an answer.
The "private" part is architecture, not a slogan. Your data lives in your own database, in your own infrastructure, behind your own access controls, on API tiers whose terms don't use your data for training. Employees see only what their permissions allow — the AI respects the same walls your people do.
What this becomes in practice: a new hire who can ask questions instead of interrupting your best person. An owner who can query a decade of records in plain English. An assistant that drafts from your documents rather than from internet vibes.
How it works
- Knowledge inventory. Where your business knowledge actually lives — files, systems, heads — and what's worth grounding the AI in.
- Ingestion and structure. Documents processed, cleaned, and indexed into a retrieval system tuned to your content types.
- The assistant. Interface built where you work (chat, in-CRM, internal tool), grounded in the index, citing its sources.
- Hard testing. We attack it with real questions — including ones it shouldn't answer — before your team touches it.
- A living system. New documents flow in; the assistant's knowledge stays current.
What you get
- A private retrieval system over your documents and data — in your infrastructure, yours
- An AI assistant that answers from your material and cites where answers came from
- Permission-aware access — the AI honors the same boundaries your team does
- An ingestion pipeline so tomorrow's documents join the brain automatically
Receipts
Our engineering credentials here are the deep end of the pool: we built an enterprise legal-AI platform end-to-end — retrieval-augmented AI at scale over legal documents, hybrid search, a 46-field extraction schema, and inbox-watching document AI that read filings, calendared deadlines, and notified the team. We also run a manifest-grounded AI assistant on the live business data of a California mortgage brokerage's CRM every day. Same discipline, sized to your business.