AI has become the fastest-growing category on this directory, and also the noisiest — a large share of "AI development" listings are general software teams that added the label without changing how they work. Here's how to tell the difference.
Ask about the boring 80%
Any team can demo a model. Ask instead how they handle data pipelines, evaluation, monitoring for drift, and fallback behaviour when the model is wrong. That's where real production AI work actually lives.
Look for integration experience, not just model experience
The hard part of most AI projects is connecting outputs to the systems where decisions happen — your CRM, your ERP, your support queue — not the model itself. A vendor that can only talk about prompting or fine-tuning, with no story for integration, will stall at the pilot stage.
Check for a point of view on cost and latency trade-offs
A team with real production experience will proactively raise inference cost and latency trade-offs rather than defaulting to the largest available model for every task. That's a reliable signal of hands-on experience versus theoretical knowledge.