Solutions · Enterprise
Your teams are already using AI. Make it consistent.
The risk in enterprise AI isn't adoption. It's fragmentation. Fifty teams, fifty prompting styles, no shared standards, no data boundaries. I set up the guidelines, guardrails, and rollout so AI behaves the same everywhere it's used.
Where AI actually helps
Shared guidelines
One written standard for how assistants behave: tone, escalation rules, what they may and may not touch. Versioned like code.
Data boundaries
Clear lines on what AI sees: which systems, which documents, which customer data. Written down, enforced, auditable.
Rollout without chaos
Pilot with one team, measure, document, then scale. Not fifty simultaneous experiments.
What I set up
- Assistant guideline documents per team, derived from how each team actually works
- A governance model: who owns the guidelines, how changes ship, how drift gets caught
- Training for team leads. Your people maintain this, not me.
- Production-grade engineering where it's warranted, documented to your standards
How it works
1. A conversation
We talk through how your business actually runs and where the time goes. No jargon, no pitch. I mostly listen.
2. I build and document it
I set up the assistant around your real workflows, in your voice, and write down how everything works in plain language.
3. Training and handoff
Your team learns to run it. Everything is yours. No subscription to me, no black box.
From real work
Recent work includes a production ID-card issuance pipeline for an enterprise client. The kind of system where documentation and handoff matter as much as the build.
Sound familiar?
Tell me about your enterpriseoperation and I'll tell you honestly whether AI helps.