Put AI features into production

The gap between a working prompt and a feature you can put in front of regulated users — routing, guardrails, audit trails, and a cost you can predict.

What’s included

• —Amazon Bedrock and SageMaker in multi-agent workflows
• —Router plus domain specialists rather than one prompt doing everything
• —Guardrails and full audit trails on every exchange
• —Durable sessions humans can join and review

How it runs

1. 01

   A call, then a written shape

   Half an hour on what you're building and what's in the way. You get back a written scope — what I'd build, in what order, and what I'd deliberately leave out.

2. 02

   Ship the smallest true thing

   A live slice teaches more than another month of planning. The first release is small, real and in production, and the plan adjusts around what it tells us.

3. 03

   Cadence you can see

   Regular releases against the roadmap, and the roadmap revisited as we go rather than once at the start. Nothing about progress should be a surprise.

4. 04

   Handover that holds

   The team ends up on foundations they can keep building on — documented, reviewed, and with the reasoning behind each decision written down.

Other ways I work

• Architect and build a platformTaking something from an idea and a deadline to a running system — the serverless backend, the frontends against it, and the release cadence that keeps it moving.
• Own a product surface end to endLonger-running work where I hold the architecture, the release cadence and the roadmap conversation — not a set of tickets, a system.
• Review an architectureA second pair of eyes on a system before it's expensive to change: where the trust boundaries actually sit, which paths to data bypass the ones you meant, what scales and what only looks like it does.