How to Choose an AI Development Company
6 min read
Choose an AI development company that starts with your business process rather than a model, can show systems running in real operations, owns integration and deployment rather than just prototypes, and explains guardrails, evaluation and handover in plain language. Demos are easy; production reliability is the differentiator.
Questions worth asking
- What business outcome will this system change, and how will we measure it?
- What happens when the AI is wrong — what is the guardrail and the escalation path?
- Which of our systems will you integrate with, and who owns the credentials?
- How will this be monitored after launch, and who is on call?
- What do we own at the end, and can our team maintain it?
Warning signs
A proposal that leads with model names rather than your workflow. No mention of evaluation or failure handling. An architecture that cannot be explained without jargon. Unwillingness to scope a small first deliverable.
What a good engagement looks like
Discovery grounded in your actual operations, a prioritised list of opportunities with effort and impact, a narrow first build that goes into real use, and then expansion based on measured results.
Frequently asked questions
- Should we hire in-house instead?
- In-house makes sense once AI is core to your product and you have continuous work. For a defined set of internal systems, a partner is usually faster and cheaper to start.
- How do we compare proposals fairly?
- Ask every vendor to scope the same first workflow, and compare their assumptions about integration, guardrails and handover — not just price.
- What should be in the contract?
- Ownership of code and data, access to the deployed environment, documentation, and a defined support arrangement after launch.