How to Hire an AI Development Agency Without Getting Burned

AI made it easy to build an impressive demo and hard to tell whether anything behind it works. A polished chat window in a sales call proves very little. Here's how to separate a team that ships AI real users depend on from one that peaks in the pitch.

Separate demos from production

A demo runs on happy-path inputs in front of a friendly audience. Production has to handle the question your model has no answer for, the retrieval that comes back empty, and the user who does something no one predicted. Ask what happens in those cases. If the answer is vague, the system is a demo.

Questions that reveal real experience

• How do you stop the model from making things up? (Good answer: grounding in your data, honest fallbacks, and human review where it matters.)
• What do you do when retrieval returns nothing relevant?
• Where does a human stay in the loop, and why there?
• How do you evaluate the model before launch, not just eyeball it?
• Can you point to AI you've put in front of real users, not just a demo?

Watch for these

• AI as decoration: a chatbot bolted on that doesn't touch your actual data.
• No talk of grounding, evaluation or fallbacks, only capabilities.
• A fixed price for AI work quoted before anyone has scoped feasibility.
• No willingness to say an idea isn't feasible yet.

What good looks like

A team that has shipped AI to production will talk about the unglamorous parts first: grounding, evaluation, fallbacks, and where a person reviews the output. They'll scope feasibility before quoting, and they'll tell you when a plain feature beats an AI one. We've rebuilt an AI feature that worked 40% of the time up to about 80%, and built agents that pause for human approval on consequential steps, and in both cases the interesting work was the safety rails, not the model.