Look for integration, not demos
A flashy prototype is easy; a system that runs in production is not. Ask to see something they've integrated into a real business and kept running — with monitoring, error handling and governance. The gap between demo and production is where most AI projects die.
They should lead with your data
A serious partner asks about your data before your use case — where it lives, whether it's clean, who can see it. If the first conversation is all model hype and no data questions, that's a warning sign.
Plan for after launch
AI systems drift, models change, and guardrails need maintenance. Ask how they monitor, update and keep the system safe over time — who gets paged when answers go wrong, how a bad model version gets rolled back, who reviews a sample of outputs each week. A team that only talks about the build, not the run, will leave you with something that quietly degrades while everyone assumes it's fine.
The criteria buyers forget to check
Three things get skipped because demos don't surface them. First, who owns the work — ask whether your account is staffed by the senior people from the pitch or handed to juniors after signing. Second, exit and ownership: do you keep the prompts, the fine-tuning data, the integration code, or does it stay locked in their platform? Third, references you can actually call, not logos on a slide. And ask how they price change — AI work is iterative, so a partner who quotes one fixed number for a moving target is either padding it or about to surprise you.
Match the firm to your bottleneck
The right partner depends on what's blocking you, not on who has the slickest deck. Pick a data-first firm when your data is scattered, messy, or not yet labelled — that work has to come before any model lands. Pick a conversational-AI specialist when the project is a customer-facing assistant under real load, where tone, escalation and latency decide whether it works. Pick a product studio when you're building an AI feature into a new app, not wiring one into an existing system. And if you also need the business itself to show up when buyers ask an AI engine for a recommendation, pick a team that does both — that pairing is rare.
Regulated and high-stakes settings
Healthcare, finance and legal change the question. Here process maturity beats model novelty. Ask where data is processed and stored, whether they'll sign the data-processing terms your compliance team needs, and how they keep an audit trail of what the AI did and why. A model that can't explain a decision is a liability in a regulated setting, however clever it is. The best signal isn't a certification logo — it's whether they raise these questions before you do.