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.

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