How to Choose an AI Development Company

Short answer: choose an AI development company on how it handles the boring parts, not on how impressive the demo looks. Ask how they will measure whether the AI is right, what happens when it is wrong, where your data goes, what it will cost to run each month and who can maintain it after launch. Good teams answer these clearly. Weak teams change the subject back to the demo.

Why AI projects go wrong

A working prototype of a chatbot or document tool can be built in days. Making it accurate enough to trust with customers, cheap enough to run at scale and safe with private data is where the real work is. Most failed AI projects did not fail at the demo. They failed in the months after it.

Questions about the problem

1. Do we actually need AI for this?

A good partner will sometimes tell you that a search box, a form or a simple rule does the job better. If every answer is "use AI", be careful.

2. How will we know if it is working?

Ask for a test set: real examples with the right answers, agreed before building starts. Accuracy should be measured against that set, not judged by how a few answers feel in a meeting.

3. What happens when it is wrong?

Every model makes mistakes. The design should show a human what the AI is unsure about, let people correct it, and never let it take an action that cannot be undone without a check.

Questions about data and safety

4. Where does our data go?

Ask which model providers will see your data, whether it is used for training, where it is stored and for how long. Get the answer in writing.

5. How do you stop it making things up?

For tools that answer questions about your business, the answer should come from your own documents, with the source shown, rather than from the model's general knowledge.

6. Who can see what?

If the tool searches internal documents, it must respect the same permissions as the rest of your systems. A junior employee should not be able to ask the assistant about salaries.

Questions about cost and ownership

7. What will it cost to run each month?

AI features have a running cost for every use. Ask for an estimate at your expected volume and at ten times that volume, and ask what can be done to keep it down.

8. Can we switch model providers later?

Models change fast. A sensible design keeps your prompts, data and logic separate from any one provider, so you can move without rebuilding.

9. Who owns the code, prompts and data?

You should, from day one, including the test sets and any fine-tuned models built for you.

10. Who maintains it after launch?

Plan for regular checks against the test set, especially when the provider updates its models. Ask what that support looks like and who does it.

Start small

The best first AI project is narrow and measurable: answering questions from your own help articles, sorting incoming enquiries, drafting replies for a human to approve. Prove value there before connecting AI to anything that touches money or customer records.

Frequently asked questions

How long does an AI feature take to build?

A focused first version usually takes a few weeks. Most of that time goes into testing it against real examples, not into the first demo.

Should we fine-tune our own model?

Rarely at the start. Most business tools work well with an existing model plus your own documents. Fine-tuning makes sense later, for narrow tasks with lots of examples.

Can AI be added to our existing software?

Usually, yes. Most AI features connect to an existing app or website through its API, without rebuilding the rest.

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