Step 4: Essential Questions to Ask
- What happens if deadlines are missed?
- What support is available after launch?
- Is ongoing maintenance an option?
- Do they have experience in your industry?
If answers are vague or unclear, proceed with caution.
Step 5: Clear Contract and Understanding Obligations
Ensure the contract includes:
- Detailed Scope of Work (SOW)
- Specific timelines and deadlines
- Payment model based on milestones
- Bug fixing responsibilities
- Intellectual property and code ownership
Common Mistakes to Avoid
- Choosing based only on price: Cheap quotes often lead to poor results.
- Skipping the scoping phase: Lack of planning causes timeline and budget issues.
- Signing without a detailed contract: Protect your project by clearly defining responsibilities and ownership.
The 7 questions — and what the answers reveal
1. Can I see something you already built that is still running?
Ask for a working production system — not a mockup, not a prototype, not a video — that a real client uses today. Request a live demo or a screen share with the client's permission. If they cannot produce one, they are at best a new company with theoretical skills, at worst a reseller with nothing proprietary.
2. What is the fixed price, and what does it include?
The quote should include what is in scope, what is not, what happens if requirements change, and a payment schedule tied to milestones — not to hours logged. If the answer is "we will quote after a paid discovery phase," ask why they need paid discovery to estimate something they have reportedly done many times.
For cost context by project type, see our guide to AI agent development costs in 2026.
3. Who specifically will build the system?
Ask for names and roles. Is there a backend engineer who has built with LLM APIs in production? A DevOps person who can deploy and monitor the system? Or will the founding partner hand the work to a freelancer after you sign?
4. What is the timeline to a working system I can test?
"Two weeks" means either the scope is trivial or someone is telling you what you want to hear. "We will start after a two-month planning phase" means the real work has not started. Ask what exactly you will be able to test and when — specifically, not vaguely.
5. What if I am not satisfied — what are my options?
Read the contract's deliverable-acceptance clause carefully. A trustworthy company will define: what "done" looks like (acceptance criteria), what happens if the delivered system does not meet them (one revision round? unlimited?), and who owns the code at each milestone. A bad contract says "payment upon delivery" with no definition of what delivery means. Exit rights — the ability to take the codebase and continue with another team — are non-negotiable on any serious engagement.
6. Which clients can I speak to directly?
Not "we can provide references upon request." Ask for a name and an introduction email before you leave the meeting. Any company with satisfied clients can do this in ten minutes. Reluctance means either clients are under NDA (legitimate, but worth probing) or the relationship did not end well. If you receive a reference, ask them one question: "Did the system do what they said it would?"
7. What do you not do — or do badly?
This is the most revealing question. A trustworthy company will name their limits honestly: "We do not build mobile apps," "We have never built a real-time voice system," "We are not the cheapest option for automations you could handle with Zapier." Evasion or overselling is a serious red flag. In AI development specifically, companies that have never worked with Priority ERP, Israeli compliance requirements, or Hebrew NLP have a real learning curve — and your project pays for it.
Technical capability checklist
Beyond the conversations, you can evaluate technical capability directly:
- Do they have a clear evaluation methodology? Ask how they measure model performance. If the answer is vague - "we test it" - that is a problem. Look for precision, recall, F1, or domain-specific metrics with defined thresholds.
- Do they understand your data privacy requirements? For Israeli companies, this includes Privacy Protection Law compliance. For fintech or healthcare, it includes additional sector-specific requirements. A vendor who cannot name the relevant regulation in your sector has not worked in it seriously.
- Can they explain their architecture choices in plain language? You do not need to understand transformer attention mechanisms. But a good vendor should be able to explain why they chose RAG over fine-tuning, why they picked a specific vector database, and what the trade-offs are - in terms a non-technical founder can follow. If they hide behind jargon, they may not understand it themselves.
- Do they have a staging environment and deployment process? Production AI systems need the same software engineering rigor as any other software: version control, staging environments, rollback plans, and monitoring. Ask what their deployment process looks like.
For context on the build-vs-buy decision - including when a custom AI system makes more sense than a SaaS product - see our build vs. buy framework. And if you are evaluating how AI agents specifically fit into your operations, our guide to AI agents for business covers the use cases and realistic timelines.
Common red flags in the Israeli market
- "We use the ChatGPT or Claude API" as the complete technical answer. Using an LLM API is the starting point, not the product. Ask about data architecture, fallback handling, evaluation, and how the system improves over time.
- Proposals that arrive right after a first call. Proper scoping requires understanding your systems, data, and workflows. Fast proposals are generic proposals.
- No mention of failure modes. Any company that presents AI as upside-only — with no mention of what happens when the chatbot misunderstands, when the LLM hallucinates, when an API is down — has not shipped in production.
- International case studies only. Israeli business context — Priority ERP, Hebrew interfaces, local labor law — is different. Ask for Israeli-specific experience if that is your environment.
Conclusion
Choosing the right software house isn’t just about pricing or technology. It’s about finding a true partner who understands your needs, communicates transparently, commits to deadlines, and delivers real value. Invest time in the selection process, and you’ll set yourself up for a smoother, more successful project outcome.
FAQ
What is the difference between an AI agency and an AI development studio?
In Israel the terms are used interchangeably, but broadly: an "agency" focuses on AI-assisted marketing, content, and packaged automation tools; a "development studio" builds custom systems — LLM integrations, AI agents, custom data pipelines — that the client owns. For most business use cases involving proprietary data or custom workflows, you want a development studio.
Do I need technical staff internally to work with an AI development company?
No, but it helps to have one person who can act as product owner — someone who can define what success looks like and evaluate whether the delivered system meets it. Pure technical handoff without internal oversight consistently leads to systems that do not fit actual workflows.
What should I ask about data privacy?
Key questions: Where does our data go when we use your system? Which LLM providers have access to it? Is there a data processing agreement (DPA)? Are you GDPR and Israeli Privacy Law compliant? A company that cannot answer these clearly has not built for enterprise-grade deployments.
