Begin with proven experience, not the length of the client list. Ask to see two or three engagements that sit close to your technology stack, machine learning development company and then ask specifically which engineers actually built it. A serious vendor will put you on a call with the tech lead. Vague answers at this stage usually mean the delivery team is not the team you were shown.

The paperwork deserves a slower read than the pitch. Three clauses do most of the work: assignment of intellectual property, non-disclosure, and termination and handover. Every artifact must transfer to you as it is paid for, including documentation, pipelines and deployment scripts. Be careful with language that leaves so-called reusable libraries with the vendor, as it is usually the dependency that makes switching painful.

Ask how they estimate. An honest estimate comes with a written set of assumptions, a breakdown by feature or module and a best case and a worst case. A fixed price works only when the scope is genuinely frozen; in any other case the provider adds a risk premium and you pay for uncertainty either way. A time-and-materials model shifts that risk to you, so it needs a cap, regular demos and transparent reporting.

The delivery process beats headcount. Establish how a new requirement enters the plan, mvp development company who signs off on a feature and what the QA setup looks like. A mature team can show you a working build every one or two weeks. Clear, written acceptance criteria are the only reliable protection against an argument at delivery time.

Last, consider the handover while the relationship is still good. Ask that the source repository stays in your organisation from day one, and that the documentation is refreshed in every sprint. A partner who is comfortable with this accepts it without argument; resistance at this point reveals most of what you need to know.