How to Evaluate AI Development Companies

If you're evaluating AI development companies, you've probably already sat through a few demos. A chatbot answering product questions. A prototype that classifies images. Maybe a dashboard showing "AI-powered insights." These demos look impressive. They're also almost completely irrelevant to whether a company can deliver a production system.

Here's what we've learned from building 13 production AI systems across five countries: the gap between a working demo and a reliable production deployment is where most projects fail. That gap is operational, not technical. It's monitoring, failure handling, data drift, edge cases, and the hundred things that only surface when real users hit the system at scale.

What to look for

Production history, not portfolio demos. Ask how many systems they have running in production right now. Not prototypes. Not pilots. Systems that real users rely on daily. Then ask how long the oldest one has been running. A team that has maintained a system for two or three years understands things a team that only ships MVPs never will — model degradation over time, schema migrations under load, and the politics of retraining schedules.

Infrastructure thinking. Any competent ML engineer can train a model. The real work is everything around it: data pipelines that don't silently break, model serving that meets latency requirements, monitoring that catches accuracy drift before users complain, and rollback procedures when a new model underperforms. Ask about their deployment pipeline. If the answer is "we deploy with a script," keep looking.

Domain depth over breadth. Be skeptical of companies that claim expertise in everything from NLP to robotics to generative AI. Real expertise is narrow and deep. A team that has spent two years building OCR systems understands character segmentation edge cases that a generalist team will rediscover from scratch — on your budget.

Red flags

They lead with model accuracy numbers. Accuracy on a test set is easy. Accuracy on real-world, messy, out-of-distribution data is the actual problem. If they quote benchmark numbers without discussing how those translate to production performance, they haven't deployed enough systems to know the difference.

No discussion of failure modes. Every AI system fails. The question is how it fails and how quickly you know about it. If a company can't describe their monitoring and alerting approach unprompted, they've probably never operated a system long enough to need one.

They want to build everything custom. Good engineering teams know when to use off-the-shelf components. If they're proposing a custom vector database when pgvector would work, or training a model from scratch when fine-tuning an existing one would suffice, you're paying for engineering ego, not results.

Questions to ask

These questions separate experienced teams from impressive presenters:

• "Walk me through a production incident with an AI system you built. What broke, how did you find out, and how did you fix it?"
• "How do you handle model retraining? What triggers it and who decides?"
• "What's your approach when the client's data quality is worse than expected?"
• "Show me your monitoring dashboards for a live system."
• "Tell me about a project you turned down and why."

The last question is particularly revealing. A company that turns down work has enough pipeline to be selective, and enough honesty to tell a client when they're not the right fit. That's the kind of partner you want for a system your business will depend on.

Assessing technical depth

Ask for a technical architecture review of a past project. Not slides — an actual system diagram with data flows, failure points, and scaling considerations. Ask them to walk through a decision they made and what they traded off. Strong teams will talk about trade-offs fluently: latency vs. accuracy, build vs. buy, real-time vs. batch. Weak teams will talk about everything being "best in class."

The best predictor of a successful AI project isn't the technology — it's whether the team has shipped and operated similar systems before. Demos are free. Production is where the work begins.