What is AI-in-production consulting?

What it is — and isn't

It's hands-on work in your repo that ends with agents doing real engineering against your standards. It isn't a slide deck, a one-off prompt workshop, or a proof of concept on a clean example. If the deliverable is a document rather than a working capability, it isn't this.

What it actually covers

Three layers seats don't give you: context (a source of truth agents can navigate), observability (you can see what the agent did), and guardrails (review gates and scoped permissions your seniors trust) — plus training your developers into the operators who run it.

How to tell it worked

Your existing team is running agents on production work without the consultant in the room. A dependency on the consultancy is a failed outcome, not a business model. The capability has to stay in your team.

Straight answers.

How is this different from an AI strategy engagement?

Strategy stops at a plan; AI-in-production consulting ends with agents running real work in your repo. A plan you can't operationalize is expensive shelfware — the value is in the working capability, not the document.

Do you do this on our codebase or a sandbox?

Your codebase, your standards, your controls. A sandbox demo dodges the exact things that break AI in production — real code, real review culture, real people.

What do we have at the end?

Your developers operating agents in production, independently. The goal is your team running this without us; that's the deliverable, not a retainer.