Why Most Founders Choose the Wrong AI Agency
Choosing an AI development agency is harder than choosing a traditional software agency. The AI market is full of teams that can demo a GPT wrapper but lack the engineering depth to build something production-worthy. The consequences of a bad choice are severe: wasted budget, months of lost time, and a codebase so poorly structured that it needs to be rewritten from scratch.
This checklist gives you the tools to evaluate AI development agencies rigorously — before you sign anything.
Section 1: Technical Credibility (10 Points)
1. Can they show you production AI code?
Not a demo, not a Loom video — actual code from a prior client project (with permission). Look for: proper async handling, error fallbacks, token cost logging, output validation. The absence of these patterns in their examples signals inexperience with production AI.
2. Do they understand model selection?
Ask which model they would recommend for your use case and why. A strong agency can articulate why GPT-4o-mini is appropriate for your classification task but Claude Sonnet is better for your document reasoning flow. Generic answers like "we use ChatGPT" are a red flag.
3. Do they have RAG and vector database experience?
Most real AI products need retrieval — letting the AI access your data, documents, or knowledge base. Ask whether they have built RAG pipelines before and which vector database they prefer (and why).
4. Can they explain their prompt engineering approach?
Prompt engineering is a craft. Ask how they version prompts, how they test prompt changes, and how they prevent prompt regressions after model updates from providers.
5. Do they build with observability from the start?
Ask what tools they use for LLM observability (LangSmith, Helicone, Braintrust, etc.) and whether observability is included in the MVP scope. If they do not know what you mean, move on.
6. Have they handled AI cost management?
At scale, LLM costs can destroy unit economics. Ask how they design for cost efficiency: model routing, caching, prompt optimization. An experienced agency has dealt with this problem before.
7. What is their tech stack preference?
Most great AI MVPs use Next.js, Supabase, and OpenAI or Anthropic. Be skeptical of unusual stack choices without strong justification. Exotic stacks add risk and complicate your future hiring.
8. How do they handle AI failures?
Ask specifically how their code handles a provider outage. The answer should involve timeouts, exponential backoff, fallback models, and graceful degradation. "We have not had that happen" is a red flag.
9. Can they build AI agents and multi-step workflows?
If your product needs autonomous AI workflows (not just single Q&A interactions), ask for examples. Agentic AI is meaningfully harder than simple LLM calls.
10. Do they have experience with your specific AI domain?
Document AI, voice AI, image analysis, code generation, and recommendation systems all have domain-specific challenges. Relevant prior experience matters.
Section 2: Delivery and Process (10 Points)
11. Do they offer a fixed-price MVP scope?
Reputable agencies can scope an MVP at a fixed price after a discovery call. Time-and-materials with no cap transfers all delivery risk to you.
12. What is their typical MVP delivery time?
A focused AI MVP should deliver in 2-4 weeks. Anything over 8 weeks for an MVP is a scope problem or a capacity problem — either way, it is your problem too.
13. Do you get direct developer access?
Will you communicate directly with the engineers building your product, or only with an account manager? Direct developer access leads to faster decisions and fewer miscommunications.
14. How often do you get progress updates?
Expect weekly demos of working software, not status reports. An agency that cannot show you running code weekly is not moving fast enough.
15. What does their discovery process look like?
A good agency runs a structured discovery session before scoping. They ask about your users, the problem, the data you have, and the constraints. Agencies that scope without discovery are guessing.
16. Do they deliver with CI/CD and deployment?
The MVP should be deployed, not just running on a developer's laptop. Ask about their deployment infrastructure and whether it is included in scope.
17. What does handoff look like?
You should receive: clean code in a Git repository you own, documentation of the architecture and key decisions, environment variable management, and a walkthrough session. Anything less is incomplete handoff.
18. Do they include testing?
Ask specifically about AI regression tests (does the AI still produce expected outputs after changes) and integration tests. Untested AI code breaks silently.
19. What are their SLAs during the build?
If you have a launch deadline, get a written commitment on delivery dates with clear milestone definitions.
20. Who owns the intellectual property?
You should own 100% of the code, models, and data processed. Confirm this explicitly in the contract.
Section 3: Business and Culture Fit (10 Points)
21. Can they provide references from recent AI clients?
Request two references from projects completed in the last 12 months. Ask references specifically about reliability, communication quality, and whether they would hire the agency again.
22. Are they transparent about their team?
Who will actually build your product? Ask for the names and LinkedIn profiles of the developers assigned to your project. Be wary of bait-and-switch where senior people sell but junior people build.
23. How do they handle scope changes?
Requirements change. A good agency has a clear, fair process for handling change requests — not a blank check for additional billing.
24. Do they understand your industry?
Domain context matters for AI products. An agency that has built AI for healthcare or fintech understands the compliance constraints. One that has not will learn on your project.
25. Do they challenge your assumptions?
The best agencies push back on your ideas when they see a better approach. An agency that agrees with everything you say is not adding value — they are avoiding conflict.
26. What is their post-launch support model?
Get clarity on what happens after delivery: bug fix responsibility, monitoring alerts, and the process for engaging ongoing work.
27. Are their proposals specific?
A strong proposal names specific features, specific technical choices, specific risks, and specific milestones. A vague proposal indicates vague thinking about your project.
28. What are the payment terms?
Typical fair terms: 30-50% upfront, balance on delivery or at milestones. Full upfront payment from a new agency relationship is a risk. Payment on delivery only (no upfront) is a sign the agency cannot operate without client capital — also a risk.
29. Is the contract clear about termination?
If the project goes badly, what is the exit process? You should be able to terminate with reasonable notice and receive all code produced to that point.
30. Do their values match yours?
You will spend weeks working closely with this team. Culture fit matters. A team that communicates proactively, owns problems, and is honest about setbacks is worth paying a premium for.
How SpeedMVPs Performs Against This Checklist
SpeedMVPs was built specifically to address the gaps in the AI agency market: direct developer access, fixed-price AI MVPs delivered in 2-3 weeks, production-ready code with observability and error handling built in, and full IP ownership for clients. Every engagement includes a structured discovery session, weekly demos, CI/CD deployment, and a comprehensive handoff package.
If you want to run this checklist against SpeedMVPs directly, book a 30-minute discovery call. We will scope your project and answer every question on this list.
