What is the biggest red flag when evaluating an AI integration partner?

A partner who defaults to fine-tuning or a custom model for every problem, rather than recommending the simplest approach (often plain prompting or RAG) that actually meets the need. This usually signals they're either overselling a more expensive engagement or lack real experience shipping production LLM applications.

How many questions should a CTO ask when vetting an AI vendor?

This checklist uses 23 questions across five categories: technical depth (8 questions), process and communication (6), portfolio and references (4), security and compliance (3), and cost and terms (2). Scoring each 0-2 gives a total out of 46, with anything above 38 indicating a strong partner and anything below 18 meaning you should walk away.

Can a partner with weak communication skills still be a good fit?

Sometimes. Weak communication or process can usually be fixed by adding more structure, such as defined overlap hours, written change requests, and named team members. Weak technical depth is a different problem: it reflects a missing capability that more process cannot fix, so it's the harder red flag to overlook.

Should the cheapest AI development proposal win?

No. The most expensive mistake CTOs make is choosing based on price alone. A cheap engagement that produces a prototype that never reaches production, or that has to be rebuilt by another team, costs more in wasted time and money than a mid-range partner who actually ships a working system.

Journal AI Integration January 20, 2026

AI Integration · Guide

How to Evaluate an AI Integration Partner: CTO Checklist

A wrong AI partner choice wastes 6 months and $50K+. This 23-question checklist across technical depth, process, and security helps CTOs pick the right one.

Anurag Verma

7 min read

AI Integration CTO Evaluation Checklist Business 2026

How to Evaluate an AI Integration Partner

Key takeaways

The checklist covers 23 questions across five categories: technical depth (8), process and communication (6), portfolio and references (4), security and compliance (3), and cost and terms (2).
The biggest technical red flag is a partner who defaults to fine-tuning or a custom model for everything instead of recommending the simplest approach that fits the need.
A partner who can't show live production AI applications, only demos, notebooks, or proofs of concept, has not proven they can ship.
Score each question 0-2; 38-46 is a strong partner, 28-37 is adequate with gaps, 18-27 has significant concerns, and below 18 means walk away.
Weak communication can be managed with more process; weak technical depth cannot be managed, because the capability itself is missing.
The most expensive mistake is choosing on price alone: a cheap engagement that fails costs more than a mid-range one that ships.

Choosing an AI integration partner is one of the highest-stakes vendor decisions a CTO makes. Get it right and you ship an AI-powered product that differentiates your business. Get it wrong and you waste 6 months and $50,000+ on a prototype that never reaches production.

We have been on both sides of this evaluation, as the agency being evaluated and as advisors helping clients evaluate other partners. This checklist comes from real experience with what matters and what does not.

Evaluating AI Partners A structured evaluation prevents the most common AI partnership failures

Category 1: Technical Depth (8 Questions)

Technical depth questions test whether a partner can actually ship production AI, not just demo it. They should cover cost management, testing strategy, failure handling, and retrieval architecture, in addition to knowing when to use prompting, RAG, or fine-tuning instead of defaulting to the most expensive option.

1. Do they understand the difference between prompt engineering, RAG, and fine-tuning?

Green flag: They can explain when each approach is appropriate and recommend the simplest option that meets your needs. Red flag: They default to “fine-tuning” or “custom model” for everything. This suggests they are either overselling or lack experience with modern LLM applications.

2. Can they show production AI applications (not just demos)?

Green flag: Live URLs, case studies with specific metrics, client references. Red flag: Only showing prototypes, notebooks, or “proof of concepts” that never went to production.

3. How do they handle AI cost management?

Green flag: They discuss token budgets, caching strategies, model routing, and include cost projections in proposals. Red flag: “We will optimize costs later” or no mention of API costs at all.

4. What is their testing strategy for AI features?

Green flag: They describe evaluation pipelines, golden datasets, semantic similarity checks, and regression testing for prompt changes. Red flag: “We test it manually” or reliance on traditional unit tests for non-deterministic AI outputs.

5. How do they handle AI failures and fallbacks?

Green flag: Every AI feature has a defined fallback behavior. They can explain their error handling and graceful degradation patterns. Red flag: No fallback strategy. “The AI will handle it” without contingency planning.

6. What LLM providers and models have they worked with?

Green flag: Experience with multiple providers (OpenAI, Anthropic, open-source models). They can articulate trade-offs between models. Red flag: Only experience with one provider, or inability to explain why they chose a specific model.

7. Do they understand embeddings and vector search?

Green flag: They can explain RAG architectures, embedding models, vector databases, and chunking strategies. Red flag: AI work limited to simple API calls without any retrieval augmentation.

8. Can they build the full product (web + AI) or just the AI?

Green flag: Integrated team that builds both the application and the AI features as one cohesive product. Red flag: AI-only consultancy that hands you a model and leaves you to integrate it.

Category 2: Process and Communication (6 Questions)

9. What is their development methodology?

Green flag: Agile with regular demos, sprint planning, and iterative delivery. Weekly or biweekly stakeholder updates. Red flag: Waterfall approach (“We will show you the finished product in 3 months”) or no defined process.

10. How do they handle timezone differences?

Green flag: Defined overlap hours, async-first communication with structured updates, clear escalation paths. Red flag: “We will figure it out” or no acknowledgment that timezone management requires deliberate systems.

11. What does their communication stack look like?

Green flag: Structured tools: project management (Linear, Jira), async updates (Slack, email), video calls (scheduled cadence), documentation (Notion, Confluence). Red flag: “Just WhatsApp us whenever” with no structured reporting.

12. How do they handle scope changes?

Green flag: Written change request process with impact assessment (cost, timeline, technical). Changes are documented before implementation. Red flag: “Sure, we can add that” to every request without assessing impact.

13. What does handoff look like?

Green flag: Documentation, knowledge transfer sessions, code walkthroughs, deployment guides, and a defined support period. Red flag: “Here is the repository” with no documentation or knowledge transfer.

14. Who will actually work on your project?

Green flag: Named team members with LinkedIn profiles and relevant experience. You meet the people doing the work. Red flag: Anonymous team. “Our developers will handle it” without specifics.

Category 3: Portfolio and References (4 Questions)

15. Can they show similar projects in your industry?

Green flag: Case studies in your domain with specific technical details and measurable outcomes. Red flag: Generic portfolio with no industry-specific experience.

16. Do their projects have live URLs?

Green flag: Working, publicly accessible applications that you can test yourself. Red flag: Screenshots only, or “the project is under NDA” for every single case study.

17. Will they provide client references?

Green flag: Willing to connect you with past clients for honest feedback. Red flag: Refuses references or provides only written testimonials.

18. What is their team composition?

Green flag: Mix of frontend/backend developers, AI/ML specialists, designers, and project managers. Clear roles. Red flag: Single-person shop claiming to do everything, or unclear team structure.

Category 4: Security and Compliance (3 Questions)

19. How do they handle data security?

Green flag: Encryption at rest and in transit, secure API key management, access controls, and data retention policies. Red flag: No security documentation or “we use standard practices” without specifics.

20. Are they compliant with relevant regulations?

Green flag: Awareness of GDPR, SOC 2, HIPAA (if applicable), and data residency requirements. Willing to sign NDAs and DPAs. Red flag: “What is GDPR?” or dismissive attitude toward compliance.

21. What happens to your data after the project?

Green flag: Clear data deletion policy. Your data is deleted or returned after project completion. Red flag: No data handling policy or claims of indefinite data retention.

Category 5: Cost and Terms (2 Questions)

22. Is their pricing transparent?

Green flag: Detailed proposal with line items, milestone-based payments, and clear definition of what is included vs. extra. Red flag: Vague pricing, “it depends” without providing ranges, or hourly billing without estimates.

23. Who owns the intellectual property?

Green flag: Full IP transfer to you upon final payment. This is clearly stated in the contract. Red flag: Agency retains IP rights, licensing restrictions, or unclear ownership terms.

Scoring Framework

Rate each question 0-2:

0: Red flag or unable to answer
1: Adequate but not impressive
2: Green flag, exceeds expectations

Score Range	Assessment
38-46	Strong partner. Proceed with confidence.
28-37	Adequate with some gaps. Negotiate improvements.
18-27	Significant concerns. Consider alternatives.
Below 18	Walk away.

Final Note

No partner will score perfectly on every question. What matters is the overall pattern. A partner who scores well on technical depth but weakly on communication can be managed with more structure. A partner who scores poorly on technical depth cannot be managed: they simply lack the capability.

The most expensive mistake is choosing based on price alone. The cheapest option that fails costs more than the mid-range option that ships. If you’ve been burned by scope or pricing surprises before, our breakdown of fixed-price vs. time-and-materials contracts is a useful companion to this checklist.

Evaluating AI partners? Schedule a discovery call. We are happy to answer all 23 questions.

Frequently asked questions

What is the biggest red flag when evaluating an AI integration partner?: A partner who defaults to fine-tuning or a custom model for every problem, rather than recommending the simplest approach (often plain prompting or RAG) that actually meets the need. This usually signals they're either overselling a more expensive engagement or lack real experience shipping production LLM applications.
How many questions should a CTO ask when vetting an AI vendor?: This checklist uses 23 questions across five categories: technical depth (8 questions), process and communication (6), portfolio and references (4), security and compliance (3), and cost and terms (2). Scoring each 0-2 gives a total out of 46, with anything above 38 indicating a strong partner and anything below 18 meaning you should walk away.
Can a partner with weak communication skills still be a good fit?: Sometimes. Weak communication or process can usually be fixed by adding more structure, such as defined overlap hours, written change requests, and named team members. Weak technical depth is a different problem: it reflects a missing capability that more process cannot fix, so it's the harder red flag to overlook.
Should the cheapest AI development proposal win?: No. The most expensive mistake CTOs make is choosing based on price alone. A cheap engagement that produces a prototype that never reaches production, or that has to be rebuilt by another team, costs more in wasted time and money than a mid-range partner who actually ships a working system.

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