I help enterprise teams find where AI truly pays, prove value on real work, and build an AI-native capability they can run without me.
An agent is only as dependable as the system and knowledge around it. We build both.
Turn business requests into verified software through orchestrated agents, human gates, and controls that hold up in production.
Give agents the context of your business through governed retrieval, knowledge graphs, and reusable skills your organization owns.
The transfer starts on day one. Every phase moves capability from my hands into yours.
Build every layer once
Your team builds a working system hands-on: agents, retrieval, knowledge graphs, orchestration, and the factory tying it together.
Shared capability, not vendor dependency.Apply it to real work
I join your team and point what we built at your backlog, documents, delivery process, controls, and production constraints.
Value in the workflow, not beside it.Make it yours
We name owners, audit controls, measure flow, and establish improvement loops. Then I step out and your team runs it.
A durable operating capability you own.No lecture-ware. Your team builds one project layer by layer, with every step verified before the next unlocks.
Move beyond chat. Direct an agent in plain language and understand the harness your team owns while models stay rented.
Put documents in, measure answers out, deliberately break retrieval, and learn to tell a real RAG system from a demo.
Model entities, relationships, and time so agents can answer questions that document retrieval alone never will.
Build verified work loops that decompose a job, execute in parallel, integrate results, and provide evidence at every gate.
Bring the layers together: one business request in, one governed, evidenced deliverable out—mapped to your real organization.
Ask of any AI initiative: what survives the model swap? Everything here does.
For more than three years I have built AI coding tools and taken agentic systems from demo to production—on top of twenty-plus years in enterprise architecture and delivery across consulting, financial services, and regulated environments.
That combination is the job: recognize where AI genuinely pays, prove it quickly on real work, and treat adoption by users and developers as the measure of success.
Every engagement ends in a running system.
Deep Claude-based tooling, built for production.
Controls are part of the architecture, not an afterthought.