Key takeaways
- Good partners start with your business problem, not their favourite tool.
- Look for engineering depth in integration, data and security — not just demos.
- Ask how quality is evaluated and how systems are supported after launch.
- Prefer clear scopes, phased delivery and verifiable references.
Many companies now offer AI services, and polished demos can make them look similar. Yet the difference between a pilot that stalls and a system that delivers value usually comes down to the partner’s approach. Use this checklist when choosing an AI development partner.
1. Discovery and business understanding
- Do they ask about your goals, processes, data and constraints before proposing a solution?
- Do they suggest a focused pilot with clear success measures?
- Will they tell you when AI is not the right answer?
2. Engineering and integration depth
Real value comes from connecting AI to your CRM, ERP, helpdesk, WhatsApp, data platforms and identity systems. Ask for examples of integrations they have delivered and how they handle legacy systems.
3. Data security and privacy
- Where will data be processed and stored?
- Which AI providers and sub-processors are involved?
- How are access, encryption, logging and retention handled?
- Can they deploy in your cloud or a private environment if required? (See private vs public AI.)
4. Quality and evaluation
Ask how they measure accuracy, test edge cases and prevent regressions when prompts or models change. A credible partner will describe an evaluation process, not just “we use the best model”.
5. Governance and responsible AI
Look for human oversight, transparency, audit trails and policy alignment built into delivery — our governance checklist lists what to expect.
6. Commercial clarity
| Model | Works well for |
|---|---|
| Fixed-scope package | Well-defined, repeatable solutions and pilots |
| Time and materials | Evolving scope and exploratory work |
| Managed service | Ongoing operation, monitoring and improvement |
Ask what is included, how AI usage costs are handled and what happens if scope changes.
7. Support after launch
AI systems need monitoring, content updates and model changes. Clarify response times, improvement cycles and who owns what.
8. References and evidence
Request references or demonstrations you can verify, and be cautious of claims that cannot be substantiated.
Questions to include in your RFP
- Describe your discovery process and typical pilot structure.
- Which systems will you integrate with, and how?
- How will our data be processed, stored and protected?
- How do you evaluate and monitor quality?
- What governance controls are included?
- What are the pricing model, assumptions and exclusions?
- What support do you provide after go-live?
Talk to XtrazCon
We are an AI-first technology partner based in Pune, India, working with businesses on strategy, engineering and managed AI services. Learn how we work, browse packages and services or tell us about your project.
Frequently asked questions
What does an AI development company do?
It helps organisations identify AI opportunities, design and build AI solutions, integrate them with existing systems, and operate and improve them after launch.
Should we build AI in-house or with a partner?
Many organisations combine both: a partner accelerates the first deployments and shares knowledge, while internal teams build long-term capability.
How much does an AI project cost?
Costs depend on scope, integrations, data readiness and support needs. A short discovery phase produces a reliable estimate before larger commitments.
What is the first step?
Start with a consultation to discuss goals and constraints, followed by a focused discovery or readiness assessment.
