Choosing an AI development company means separating teams that ship software from teams that only polish demos. Ask how they measure quality, handle data and keep models safe in production.
Proof points that beat buzzwords
- Production use cases with before/after metrics
- Evaluation sets and regression testing process
- Clear data retention and PII handling policy
- Guardrails and human escalation paths
- Ability to integrate with your CRM/APIs — not a standalone chat
Interview prompts that reveal depth
- Show an evaluation failure you caught before go-live
- How do you version prompts and tools?
- What happens when the model is wrong with a customer?
- Who owns embeddings, logs and vendor API keys?
- How do you budget ongoing model spend?
Build vs buy vs wrap APIs
Good partners recommend the lightest path: API wrap, RAG layer or full AI agents. For feature work inside existing products, see AI development and adding AI to existing software.
Red flags in AI proposals
- Accuracy guarantees without an evaluation plan
- No mention of logging, rate limits or cost controls
- “We fine-tune everything” as the default answer
- Opaque subcontracting of model work
Selection workflow that protects budget
Start with a paid pilot that has a kill criterion. Require weekly demos against a fixed eval set. Expand only when contain rate and escalation quality meet your bar.
Pressure-test an AI engagement
Book a call or review AI services to see how we structure pilots.
