How to Evaluate an AI Development Partner (Before You Sign Anything)
Most AI development companies look the same when you first evaluate them. Everyone has a ChatGPT demo, case study PDFs, and a deck with the word "production-ready" somewhere in it.
The companies that can actually ship AI into production are a smaller group. Here's how to tell the difference.
Start with the work, not the pitch
Ask to see a specific technical deliverable from a previous engagement — not a case study, not metrics. Something concrete: an architecture diagram, a deployment runbook, a code review checklist they actually used.
A company that has shipped real AI work will have artifacts from that work. They'll be able to share something without pausing to figure out what it is or redacting so much that it becomes meaningless.
If the answer is "we can't share anything due to NDAs," that might be true — but it's also what you'd say if you didn't have much to show. Push for something. Even sanitized architecture diagrams reveal whether a team has thought carefully about a problem or is describing what they plan to do.
Ask who actually does the work
Many AI development agencies have a thin layer of experienced people at the top and a team of junior engineers doing the actual build. This isn't always a problem — but you need to know which model you're buying into.
Specifically, ask: Who will design the architecture? Who will review code before it ships? Who do I call when something goes wrong at 2am?
Then verify. Ask to meet the person who will own your project technically. Ask them a hard question about your specific problem. If they can engage with it in real-time and identify the things they'd need to know before giving you a real answer, that's a good sign. If they defer to a "we'll scope that out after kickoff" response for every technical question, you've found a layer of abstraction you didn't want.
Ask about the projects that didn't go well
Every team that ships real work has experienced failure. They've had to renegotiate scope, discovered something wrong mid-build, made a decision they later regretted.
Ask about a project that went sideways and what they did about it.
The answer tells you two things. First: whether they're honest with clients when things go wrong, or whether they tend to discover problems too late. Second: whether they have the experience to recognize bad situations early and the process to resolve them.
If every past project was smooth sailing, either they haven't shipped much or you're getting a rehearsed answer.
Understand their position on scope
AI projects scope-creep more than traditional software projects. The problem is harder to define upfront, stakeholders' expectations evolve as they see what's possible, and the technology surface is large enough that it's easy to get distracted.
Ask how they handle scope changes.
A team that has done this before will have a clear answer. They'll describe how they define the initial scope, what happens when something is out of scope, and how they prevent the "just one more thing" conversations from derailing a build.
A team that answers with "we're flexible" or "we figure it out as we go" may be telling you that they don't charge for overruns — or they may be telling you that they don't track scope at all. Ask which one it is.
Ask about the compliance or operational requirements your project carries
If your project touches healthcare data, financial transactions, user PII, or regulated environments, ask how the team has handled those requirements before.
This isn't primarily about whether they've done healthcare AI or fintech before. It's about whether they understand that compliance and security requirements shape architecture decisions — and that those decisions need to be made early, not retrofitted.
A team without this experience will often say "we'll bring in a compliance consultant later." That's the wrong approach. The time to make HIPAA-aware architecture decisions is before the schema is designed, not after the system is built. Ask at what stage compliance requirements get incorporated into the technical work.
What to look for in a proposal
A good proposal does three things: it demonstrates they understood your problem, it describes a process you can follow, and it's honest about what it doesn't know yet.
Watch out for proposals that seem to answer every question before the real scoping work has been done. Either the company is telling you what you want to hear, or the proposal is a template that doesn't reflect your specific situation.
A proposal that says "here's what we'll build in the first two weeks, and here's what we'll define together during that time before committing to the rest" is usually more trustworthy than one that maps out the entire 12-week engagement in week-by-week detail before anything has been discovered.
The questions worth asking every finalist
- What's the most important technical decision we'll make in the first two weeks, and how will you approach it?
- Tell me about a time a client was unhappy mid-project. What happened and what did you do?
- What does your handoff look like? How do we maintain what you build after you're gone?
- What would make you say no to this project?
That last one is worth listening to carefully. A company that has declined work before — because the fit wasn't right, the timeline wasn't realistic, or the client wasn't ready — is a company that cares about shipping successfully. If every project is the right project, that's a flag.
If you're evaluating AI development partners and want to understand whether our approach fits your situation, a 30-minute conversation costs nothing. We'll tell you honestly if we're not the right fit.