How to Hire an AI Development Company: A Practical Buyer’s Checklist

Start with the job, not the model

Good AI vendors ask which workflow you want to change and how you will measure success. Weak vendors open with a model name and a slide about “transformation.” Hire partners who can say no to bad use cases.

Proof they ship production AI

Ask for evaluation sets, latency and cost metrics, human-in-the-loop design, and monitoring after launch. A chatbot demo without citations, ACL handling, or failure modes is not production experience.

Data and security readiness

Confirm how they handle PII, where prompts and embeddings are stored, and whether they can work with your SSO and document permissions. For Sri Lankan banks and agencies, data residency and audit logs are non-negotiable.

Commercial structure that protects you

Prefer a paid discovery or fixed pilot with kill criteria over a large vague SOW. Own your prompts, evaluation data, and application code. Clarify model API costs separately from engineering fees.

• Pilot success metrics written before build
• IP assignment for custom code and configs
• Exit plan if the vendor relationship ends
• AMC or ops handover after production

Red flags

Guaranteed accuracy claims, no discussion of failure modes, refusal to share who does the engineering, and “we fine-tune everything” as a default. Also avoid vendors who cannot explain RAG vs fine-tuning trade-offs in plain language.