Choosing an AI Development Partner: A Practical Checklist

Selecting a development partner for an AI project looks like a technology decision and behaves like an operations decision. The models are largely commodity; what varies enormously is whether a team can put one into production, keep it accurate, and hand it over cleanly.

Ask for evidence of production, not prototypes

A convincing demo takes a week. A system that stays accurate for a year takes discipline. Ask specifically about work that is live: how it is monitored, how often it is retrained or re-prompted, what broke in the first month, and who fixed it.

The answer to "what went wrong in production?" is the single most informative question in a vendor conversation. A partner with real deployments answers it immediately and in detail. A partner with only pilots changes the subject.

• Two or more references for systems live longer than six months.
• A description of their evaluation process, with numbers.
• A concrete incident and how it was diagnosed and resolved.

Insist on evaluation before implementation

Any serious AI engagement should define what "good" means before a line of production code is written. That means a labelled evaluation set drawn from your real data, an agreed accuracy or quality threshold, and a plan for what happens when the system drops below it.

Without this, acceptance testing degenerates into opinion. With it, both sides can tell whether the thing works, and the same harness keeps working as a regression check for years afterwards.

If nobody can tell you how the system will be scored, nobody can tell you when it is finished.

Get the ownership terms right on day one

Ownership questions are cheap to settle at contract stage and expensive to settle later. Who owns the prompts, the fine-tuned weights, the evaluation sets and the labelled data? Where does inference run, and what happens to your data at the provider? Can you export everything and run it yourself if the relationship ends?

Also settle the model-change question: providers deprecate and update models on their own timetable, and the contract should say who is responsible for re-validating the system when that happens.

• Source, prompts, evaluation data and fine-tunes transfer to you.
• Data residency and retention are named explicitly, not by reference.
• Someone is contractually responsible for model deprecation and revalidation.

Watch how they handle the boring parts

Logging, cost controls, rate limiting, retries, PII redaction and access control are unglamorous and are exactly where production systems live or die. A partner who raises these before you do is telling you something useful about how they work.

The same applies to handover. Ask what documentation and runbooks you will receive, and whether your own engineers will be able to make a change three months after the engagement ends without calling anyone.

Key takeaways

• Weight production references far above demo quality.
• Agree an evaluation set and a quality threshold before build starts.
• Settle IP, data residency and model-deprecation responsibility in the contract.
• Judge a partner by how seriously they take logging, cost control and handover.