AI on your own data
AI that knows its limits, and yours.
Assistants and automations grounded in your own documents and data, scoped to answer only what they know, and handing the rest to a human fast.
Tell me what you are buildingIt is right when.
- Your team answers the same questions from the same documents every day
- Inbound email or tickets need sorting before anyone can act
- Your chatbot guesses, and customers notice
- You want a pilot that proves value before a bigger bet
AI will not fix a process you cannot explain. Point it at a workflow nobody agreed on and it automates the disagreement, so the process gets mapped first.
“A bot that guesses is worse than a quiet contact form.”
How it works.
- DiscoveryWe map the problem and the one outcome that matters most.
- Fixed-scope first phaseOne outcome, one price, agreed up front.
- Demos at every stepYou see working software, not status reports.
- You own everythingYour repository, your keys, your data.
- Decide at each milestoneContinue, pause or stop, and keep what you paid for.
Proof, not promises.
The AI twin on this site is one of these: built on my own projects and writing, scoped to what it knows, and handing everything else to me.
Talk to it →Read before you decide.
In-depth guides, each with a working Field Kit.
Build a RAG Assistant on Your Own Data: A Technical Guide
A technical guide to building a retrieval augmented generation assistant grounded in your own documents. Why RAG beats fine-tuning for most businesses, the inge…
Includes the RAG Architecture Blueprint → GuidePutting an LLM in Production Safely: A Technical Guide
A technical guide to taking an LLM feature from demo to production. Treating prompt injection as a real threat, controlling cost and latency, planning for failu…
Includes the LLM Production Readiness Checklist → GuideStructured Output and Tool Calling: Making an LLM Act on Your Systems
A technical guide to getting reliable structured data out of an LLM and letting it safely call your functions. Why parsing free text fails, JSON schema output w…
Includes the Tool-Calling Design Checklist →Questions.
Where does our data go?
The assistant is grounded in your documents with permissions respected, and nothing is used beyond what the build needs. The architecture is agreed before a line is written.
How do you stop it from making things up?
Scope and grounding. It answers from retrieved sources with citations, refuses what it cannot support, and hands off to a person.
How do we start small?
A pilot on one workflow with one measurable outcome. It either earns the next phase or it does not, and you keep what was built either way.