How to evaluate an AI development partner

Choosing a partner to help with AI development is a different kind of decision than choosing one to build a normal web app. A normal build has a well-understood scope. An AI build often does not, because what you are buying is not a finished product but a team's judgment about how to get from an uncertain starting point to something that works reliably. That makes the evaluation harder, and it makes a bad choice more expensive.

Ask about production, not demos

The single most useful question you can ask a potential partner is what share of their AI engagements have actually reached production and stayed there. Anyone can produce an impressive demo; a demo proves almost nothing about whether a team can turn a demo into something real users depend on. A partner who has a real answer to this question, with real examples, has done the hard part before. One who deflects to talking about the technology instead is telling you something too.

This connects directly to the demo-to-production gap covered in the AI last mile. A partner worth hiring should be able to speak fluently about that gap from experience, not treat it as a surprise.

Ask specifically how they review AI-generated code

Every serious AI development team uses AI coding tools now. The differentiator is not whether they use them, it is what happens between the AI producing output and that output reaching your users. Ask directly: what does your review process look like, who does it, and what does it catch. A vague answer, or an answer that amounts to "we trust the tools," is a real warning sign given everything covered in the real risks of taking vibe-coded software to production.

A strong answer sounds specific: automated checks first, then a senior engineer reading for assumptions and fit, tests added for the flows that matter, and a clear owner accountable for each merge. If a partner cannot describe their process at that level of detail, they may not have one.

Watch for the six-month pattern

The pattern that harms companies most often looks the same across accounts: an impressive sales process, a compelling proposal, a confident kickoff, and then months later a codebase that cannot scale, a system that only works on curated demo data, and a partner who has already moved on to selling the next engagement. The warning signs are visible earlier than the six-month mark if you know to look: vague answers about production readiness, no clear point of contact who will still be on the project in month four, and a proposal that reads like a technology showcase rather than a plan tied to your actual problem.

Ask about their governance thinking

A partner who has never had to think about AI coding governance has probably never operated at the scale where it matters, which may be fine for a small project and a real gap for anything larger. Ask how they would handle approval for changes touching production, what they log, and how they would help you set up the same kind of baseline covered in enterprise AI coding governance. Partners without a real answer here are operating without safety checks, whatever the sales deck says.

Define your own success first

Before any vendor conversation, be clear internally about what success looks like: faster decisions, fewer manual steps, a specific operational metric moved, not "an AI feature" as an end in itself. Partners without clear governance frameworks, documentation standards, or a way to measure whether their work actually helped are relying on hope rather than evidence, and that shows up eventually in the relationship. A partner who asks you this question before pitching a solution is usually a better sign than one who jumps straight to the technology.

Where Reveneau fits

We answer the production question directly, because we would rather lose an engagement to an honest no than win one on a demo that will not keep working. Our AI development work is built around the review and governance practices covered throughout this guide, and if you want to see how we would answer these exact questions for your situation, talk to us. The main guide ties this evaluation back to the whole path from AI-generated code to something you can run a business on.