How to choose an AI development company

Start with your goal, not the technology

Before you speak to any supplier, define what success looks like in business terms. "Reduce first-response time on support tickets" or "cut the time staff spend searching contracts" is far more useful than "we want a chatbot" or "we need an LLM". A clear goal lets you judge whether a supplier is solving your problem or selling a favourite tool.

Note the systems involved, the data available, the people who will use the result and any regulatory constraints. Decide who in your organisation owns the project and can make decisions quickly. This preparation shortens discovery, makes quotes comparable and quickly reveals which companies ask thoughtful questions and which simply agree with everything.

A checklist for evaluating AI development companies

Use the checklist below to compare shortlisted suppliers on equal terms. No single item decides the matter, but a company that scores well across most of them is likely to deliver reliably. Ask for evidence wherever possible: case studies, references, sample documents and access to the people who would actually work on your project.

Weight the criteria according to your situation. A regulated business should give security, data handling and auditability more weight than speed. A startup testing an idea may care most about rapid iteration and a low-cost pilot. Agree the weighting internally before meetings begin, so that a confident presentation does not outweigh the factors that matter most to you.

• Production experience: AI systems in live use, with measurable results the supplier can explain in detail.
• Full-stack capability: integration, web or app development, data engineering and hosting, not only model work.
• Evaluation practice: a defined way to test accuracy, safety and performance before and after launch.
• Security and privacy: clear data handling, access controls, hosting options and GDPR awareness where relevant.
• Communication: a named lead, regular demos, transparent reporting and plain-English explanations.
• Support: monitoring, maintenance and a response process once the system is live.
• Ownership: clarity on who owns the code, prompts, data and models at the end of the engagement.

Questions to ask before you sign

The answers to these questions reveal more than a polished sales presentation. Strong suppliers answer them specifically, with examples, and are open about limitations. Weak answers tend to be generic, overly confident or focused on tools rather than outcomes. Put the same questions to every shortlisted supplier so the answers can be compared directly.

It also helps to ask to speak with a client reference and with the engineer or project lead who would run your work. Ask them to describe a project that did not go to plan and what they changed as a result. How a team handles problems is often a better predictor of success than how it describes its successes.

• How would you measure whether this system is working well, and how often?
• What happens when the model is uncertain or wrong?
• Where will our data be processed and stored, and which third parties will see it?
• Which parts of the solution depend on a specific model provider, and can they be switched?
• What will it cost to run each month after launch, and what drives that cost?
• What do you need from our team, and how much of their time?

Red flags to watch for

Some warning signs appear early. Treat any of the following with caution, and ask the supplier to explain before proceeding. One red flag is not always disqualifying, but several together usually indicate a mismatch in experience, honesty or working style that will become expensive later in the project, when changing supplier is far harder.

• Guaranteed accuracy, savings or revenue figures before the supplier has seen your data.
• A fixed price for a vague scope, with no discovery phase or written assumptions.
• No mention of testing, evaluation or how errors will be detected and handled.
• Reluctance to explain data handling, hosting or subcontractor arrangements.
• Proprietary lock-in with unclear ownership of code, prompts or trained models.
• Only demos and prototypes, with no systems running in production.
• Pressure to sign quickly or to commit to a large programme before a pilot.

Choosing an engagement model

How you contract matters as much as whom you hire. The right model depends on how well defined your requirements are and how much you expect them to change. Many successful AI projects begin with a short, fixed-price discovery or pilot, then move to a more flexible model once the approach is proven.

Whichever model you choose, agree how changes will be requested, estimated and approved, and how often progress will be demonstrated. AI work involves genuine uncertainty, because model behaviour on your data is only fully known once it has been tested. A contract that acknowledges this, with checkpoints where scope can be adjusted, protects both sides better than one that pretends otherwise.

What a good proposal contains

A good proposal reads like a plan you could hand to a colleague. It restates your goal in business terms, defines what is in and out of scope, and lists the assumptions on which the price depends. It explains the technical approach in plain language, including how the system will be evaluated, secured and supported after launch.

It should also include milestones with demonstrable outputs, the team and their roles, a breakdown of one-off and recurring costs, risks with proposed mitigations, and clear terms on ownership and handover. If a proposal leaves you unsure what you will receive, when or at what total cost, ask for clarification before signing. Gen7Ven is happy to talk through your requirements if you would like an initial view.