The Stakes Are High
Hiring the wrong AI development company doesn't just waste money. It wastes 6-12 months of your roadmap, burns team morale, and makes leadership skeptical of AI altogether. One bad hire can set your AI strategy back by years.
The problem? Most businesses don't know what questions to ask before hiring an AI developer. They evaluate AI companies the same way they'd evaluate a web design agency, and that's a recipe for disaster. AI projects have unique risks around data, model drift, ongoing costs, and accuracy that traditional software doesn't.
These 15 questions will help you separate genuine AI expertise from marketing hype. Print this list, bring it to every vendor call, and pay close attention to how they respond.
The 15 Questions
"Can you show me 3 production AI systems you've built?"
Why It Matters:
The AI space is flooded with developers who've built demos but never shipped anything to real users. A proof of concept that works in a notebook is fundamentally different from a production system handling thousands of requests per day. You need someone who has solved the hard problems: latency, error handling, monitoring, scaling, and cost management in live environments.
What Good Answers Look Like:
They can name specific clients (or describe anonymized case studies), explain the business problem each system solved, share metrics like uptime, throughput, and ROI. They offer to connect you with references. They talk about the challenges they hit in production and how they solved them.
Red Flag:
They only show mockups, Figma screens, or demo videos. They say "we've built lots of things" but can't get specific. Their portfolio is all internal tools or hackathon projects. No references available.
"What AI frameworks and models do you work with?"
Why It Matters:
The AI ecosystem is vast and changing fast. A competent AI developer needs to be fluent across multiple models, frameworks, and approaches. Different problems require different tools. Someone who only knows one model will try to force every problem into that model's shape — and that leads to bad outcomes.
What Good Answers Look Like:
They mention specific models (OpenAI GPT-4o, Anthropic Claude, Google Gemini, Llama, Mistral) and can explain the tradeoffs between them. They reference orchestration tools like LangChain, LlamaIndex, or Google ADK. They discuss when to use fine-tuning vs. RAG vs. prompt engineering. They have opinions about which tools are best for which use cases.
Red Flag:
They only mention "ChatGPT" or "AI" generically. They can't explain why they'd choose one model over another. They have no experience with anything beyond wrapper apps on top of a single API.
"How do you handle data privacy and security?"
Why It Matters:
AI systems process your business data, customer information, and potentially sensitive documents. A security breach or data leak doesn't just cost money — it destroys trust. Your AI partner needs to treat data security as a first-class concern, not an afterthought.
What Good Answers Look Like:
They discuss encryption at rest and in transit. They explain access controls and least-privilege principles. They ask about your data residency requirements. They mention compliance frameworks relevant to your industry (GDPR, HIPAA, SOC 2). They explain how they handle data in AI model training — whether your data is used to train third-party models and how to prevent that.
Red Flag:
Vague answers like "we take security seriously." No mention of specific practices. They've never heard of data processing agreements. They can't explain where your data goes when it's sent to an AI model.
"What's your discovery and scoping process?"
Why It Matters:
The number one reason AI projects fail is building the wrong thing. A structured discovery phase — where the developer deeply understands your business processes, pain points, data landscape, and success criteria — is what separates successful projects from expensive failures. Discovery typically costs 10-15% of the project but prevents 80% of failures.
What Good Answers Look Like:
They describe a clear process: stakeholder interviews, workflow mapping, data audit, gap analysis, then a scoped proposal with defined deliverables, timeline, and success metrics. They push back on vague requirements. They ask more questions than you do in the first meeting.
Red Flag:
They start quoting a price and timeline after a single call. They jump straight to "we'll build you a chatbot" without understanding the underlying problem. No discovery phase in their process.
"How do you price projects?"
Why It Matters:
AI project pricing is notoriously opaque. Without clarity on the pricing model, you risk budget overruns, scope creep, or paying for work you didn't need. Understanding how they charge helps you compare vendors accurately and plan your budget realistically.
What Good Answers Look Like:
They clearly explain their model: fixed price, time and materials, or milestone-based. They break down what's included (discovery, development, testing, training, deployment) and what's extra (additional features, ongoing support). They provide a range based on similar projects. They explain how change requests are handled and priced.
Red Flag:
"It depends" with no framework for how they estimate. No written proposals or statements of work. Pricing that seems suspiciously low (they'll make it up with change orders). No discussion of what happens when scope changes.
"What are the ongoing costs after launch?"
Why It Matters:
Unlike traditional software, AI systems have significant recurring costs. API calls to models like GPT-4o or Claude can add up quickly. Hosting, vector databases, monitoring tools, and maintenance all contribute to the total cost of ownership. Many businesses are blindsided by these costs because their developer only quoted the build cost.
What Good Answers Look Like:
They provide a detailed breakdown: estimated API costs per month based on expected usage, hosting/infrastructure costs, maintenance and monitoring costs, and support retainer options. They explain how to optimize costs as usage scales. They've thought about caching, model selection, and cost controls.
Red Flag:
They only quote the build cost. They've never estimated API costs for a client. When you ask about ongoing costs, they say "it shouldn't be much." No mention of monitoring or maintenance.
"How do you handle model updates and API changes?"
Why It Matters:
AI models change rapidly. OpenAI, Anthropic, and Google release new model versions regularly — and sometimes deprecate old ones with limited notice. A model that works perfectly today might behave differently after an update, or its API might change entirely. Your system needs to be built to handle this reality.
What Good Answers Look Like:
They architect systems with model abstraction so you can swap providers without rebuilding. They have a testing strategy for model updates. They monitor model performance and alert you when outputs degrade. They stay current with the AI landscape and proactively recommend upgrades when better options become available.
Red Flag:
"We'll cross that bridge when we come to it." Hard-coded API calls with no abstraction layer. No awareness of model versioning. They've never dealt with a breaking API change.
"What happens when the AI gives wrong answers?"
Why It Matters:
AI hallucinations are real. Every large language model will occasionally produce confident, convincing, and completely wrong outputs. This isn't a bug — it's a fundamental characteristic of the technology. The question isn't whether your AI will make mistakes. The question is what happens when it does. A wrong answer in customer support, financial analysis, or medical information can have serious consequences.
What Good Answers Look Like:
They discuss specific guardrails: output validation, confidence scoring, source citation, fallback to human review for low-confidence answers. They build monitoring dashboards that track accuracy over time. They implement human-in-the-loop workflows for high-stakes decisions. They've dealt with hallucinations before and can describe their mitigation strategies.
Red Flag:
"AI doesn't really make mistakes if you prompt it right." No plan for error handling. No monitoring for output quality. They've never heard of hallucination mitigation. No concept of human-in-the-loop.
"Do you provide training for our team?"
Why It Matters:
The most technically brilliant AI system is worthless if your team doesn't use it. Adoption is the single biggest determinant of ROI. Teams that don't understand what the AI can and can't do will either underuse it (wasting your investment) or misuse it (creating risk). Training isn't a nice-to-have — it's essential infrastructure.
What Good Answers Look Like:
Training is included as a standard deliverable, not an upsell. They provide hands-on workshops, written documentation, and video walkthroughs. They train both end users and administrators. They offer ongoing support options (office hours, Slack channel, or dedicated contact) for the first 30-90 days post-launch.
Red Flag:
No training included in the project scope. "It's pretty intuitive — your team will figure it out." Training is a separate, expensive add-on. No documentation provided.
"What's your typical project timeline?"
Why It Matters:
Unrealistic timelines are a leading cause of AI project failure. If someone promises a fully custom AI system in two weeks, they're either cutting corners or they don't understand the complexity. On the other hand, projects that drag on for 6+ months without delivering value lose stakeholder support and often get cancelled.
What Good Answers Look Like:
They give realistic ranges based on project complexity: simple integrations in 4-6 weeks, custom AI systems in 8-12 weeks, enterprise solutions in 3-6 months. They break the timeline into phases with milestones. They explain what factors affect timeline (data quality, integrations, compliance requirements). They aim to deliver a working MVP fast and iterate.
Red Flag:
They promise everything in 2 weeks regardless of complexity. Or conversely, they quote 6+ months for a straightforward project with no phased delivery. No milestones or checkpoints — just a big delivery at the end.
"Can you integrate with our existing systems?"
Why It Matters:
AI doesn't exist in a vacuum. For it to deliver real value, it needs to connect to your CRM, ERP, databases, communication tools, and existing workflows. A standalone AI tool that requires manual copy-paste between systems will never achieve meaningful adoption or ROI.
What Good Answers Look Like:
They ask about your current tech stack before proposing solutions. They have experience with common business platforms (Salesforce, HubSpot, SAP, Slack, Teams, etc.). They understand APIs, webhooks, and data pipelines. They can explain how the AI system will read from and write to your existing tools. They consider data sync, authentication, and error handling in integrations.
Red Flag:
They only build standalone tools. They've never integrated with a CRM or ERP. "We'll give you an API endpoint and you can figure out the integration." No interest in understanding your existing systems.
"How will we measure success?"
Why It Matters:
If you can't measure it, you can't improve it — and you can't prove the investment was worthwhile. Too many AI projects launch without clear success criteria, making it impossible to know whether the system is delivering value or just consuming budget. Defined KPIs before development starts keep everyone aligned and accountable.
What Good Answers Look Like:
They insist on defining KPIs during discovery — before a single line of code is written. They suggest specific, measurable metrics: response accuracy rate, time saved per task, cost reduction percentage, customer satisfaction scores, ticket deflection rate. They build dashboards and reporting into the deliverables so you can track these metrics in real time.
Red Flag:
No mention of measurement. "You'll see the difference." No analytics or reporting built into the system. They can't articulate what success looks like for your specific project.
"What's your communication process during the project?"
Why It Matters:
Poor communication kills projects. If you hand over a deposit and don't hear from the developer for weeks, you have no idea if the project is on track, off the rails, or abandoned. Regular communication catches misalignments early when they're cheap to fix, instead of late when they require expensive rework.
What Good Answers Look Like:
They outline a clear cadence: weekly progress updates, bi-weekly demo sessions, a shared Slack or Teams channel for quick questions, and a project management tool (Jira, Linear, Notion) where you can see task progress in real time. They assign a dedicated point of contact. They proactively flag risks and blockers before they become problems.
Red Flag:
"We'll check in when there's something to show." No project management tool. No regular updates. You have to chase them for status. Communication only happens at the start and at delivery — nothing in between.
"Who will actually work on our project?"
Why It Matters:
The person on the sales call is rarely the person writing the code. Some agencies win contracts with senior talent and then hand the work to junior developers or subcontractors. There's nothing inherently wrong with junior developers, but you need to know who's building your system and what their experience level is. AI development requires specialized skills that take years to develop.
What Good Answers Look Like:
They introduce the actual team members who will work on your project. They share relevant experience and backgrounds. Senior developers lead the architecture and key technical decisions. If they use subcontractors, they're transparent about it. The person you talk to in sales continues to be involved in the project.
Red Flag:
Vague about who does the work. "Our team" with no names or backgrounds. The senior person disappears after the contract is signed. They outsource everything but present it as in-house work. No opportunity to meet the actual developers.
"What if we're not happy with the results?"
Why It Matters:
No project goes perfectly. Requirements evolve, expectations shift, and sometimes the first approach doesn't work. What matters is how the developer handles this. A clear revision and satisfaction process protects both parties and prevents disputes. It also tells you a lot about their confidence in their own work.
What Good Answers Look Like:
They have a defined revision process built into the contract: a set number of revision rounds, clear criteria for acceptance, and an escalation path. They offer milestone-based payments so you're not paying for the full project upfront. They discuss exit terms — what happens to your code, data, and IP if the relationship ends. They stand behind their work.
Red Flag:
No revision policy. "All sales are final." They require 100% payment upfront. No clear handoff process if things don't work out. They get defensive when you ask about what happens if you're unsatisfied.
Bonus: The Ultimate Red Flag
There's one red flag that trumps all others: if they can't explain AI concepts in plain English, they probably can't build it either.
Genuine experts simplify. They can explain RAG, fine-tuning, embeddings, and agent architectures in terms a non-technical CEO would understand. They don't hide behind jargon because they don't need to. Their confidence comes from competence, not vocabulary.
Pretenders complicate. They use buzzwords to impress rather than inform. They can't explain why they'd choose one approach over another. They speak in abstractions because they don't have concrete experience to draw from.
Pay attention to how you feel after a conversation with a potential AI partner. Do you feel informed and empowered? Or confused and dependent? The right partner makes you smarter. The wrong one keeps you in the dark.
How to Use This Checklist
Don't just read these questions — use them. Here's the practical approach:
- Send this list to every vendor before the first call. Tell them you'll be asking these questions. Serious companies will welcome the transparency. Unqualified vendors will self-select out.
- Score each answer on a scale of 1-5. Create a simple spreadsheet. Compare vendors objectively instead of going with gut feeling or the flashiest presentation.
- Weight questions differently based on your priorities. If data security is critical for your industry, weight question 3 heavily. If you need fast delivery, weight questions 10 and 13.
- Check references for your top 2 candidates. Ask their previous clients the same questions and see if the answers match.
The company that scores highest across all 15 questions is almost certainly the right partner. Not the cheapest. Not the one with the best website. The one that demonstrates real expertise, transparency, and a genuine commitment to your success.
Ask Us These 15 Questions
We welcome the hard questions. Book a free call and grill us — that's how you find the right partner.
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