How to choose the right AI software development company

Get clear on the problem before you go solution-hunting

Before you even start talking to AI development partners, slow down. Great AI projects don't start with "let's use machine learning". They begin with clarity.

You need to ask some real, sometimes uncomfortable questions:

• What's broken or inefficient in our process?
• Are we trying to automate something? Predict something? Improve the customer experience?
• Do we even have usable data for this?

Without that clarity, it's easy to fall into the trap of buying into a fancy AI solution that doesn't fix anything. We set out why this step comes first in our piece on settling AI strategy before any code is written.

Our approach:

At Imobisoft, we don't show up with a deck full of pre-baked answers. We sit down with you to figure out if there's even a real use case worth solving. Sometimes that means saying, "This doesn't need AI", and that's a win. We'll look at your data early and often to make sure it can support the kind of solution you're aiming for. No fluff, just clarity. That is the starting point of our AI strategy and consulting work.

Make sure they actually know what they're doing

A lot of companies claim to "do AI". But the real question is: have they made it work when it matters?

It's one thing to know the theory. It's another to ship AI systems that solve real problems, under real constraints, in messy, high-stakes environments.

Don't just ask what models they've trained. Ask what problems they've solved. Where did their system run? Who used it? Did it actually move the needle?

You want a team that's comfortable working with the right tools for the job, but more importantly, you want people who understand why those tools matter, and when not to use them. A proof of concept is not the same as a system in live use, and the gap between the two is where many projects stall, as we explain in our guide to moving AI from proof of concept to production.

Real-world proof: At Imobisoft, we developed an AI platform for pharmaceutical companies to manage regulatory risk. Apercus supports pharma teams in following the ABPI Code of Practice, with AI-driven compliance feedback while content is written, checks before medical, legal and regulatory review, and monitoring of websites and social media after publication.

That's not just AI for the sake of it. That's AI saving time and catching risks.

Look for proof, not promises

Talk is cheap. Any AI company can say they "deliver results", but can they show you?

You want to see real-world case studies that walk through what they actually built, how they built it, and what happened afterwards. Ask for:

• A clear project storyWhat was the problem? What was the plan? How did it go?
• Tangible outcomesDid they reduce manual effort, cut processing time or improve forecasting accuracy, and by how much?
• Work in your spaceHealthcare, utilities, manufacturing: whatever your world is, they should at least speak the language.
• Similar constraintsHave they worked with similar data volumes, security requirements and industry rules to yours?
• References you can callAsk to speak to a past client whose project looked like yours.

A good case study includes the business outcome and the lessons learnt, not just the tech stack. You can see how we write ours on our case studies page.

If a team can't show you concrete examples of what they've built and the results they've delivered, that's a red flag. You need proof, not just a pitch.

Don't overlook how they handle data (It'll come back to bite you)

Let's be real: AI is only as good as the data behind it. So, before you sign anything, dig into how the company handles your data.

Ask them things like:

• How do they clean and prep raw data?
• Do they have ways to spot bias, or are they just hoping it doesn't show up?
• Can they explain how their models work, or is it all a black box?
• Are they actually following privacy laws like GDPR, or just saying they do?

If you're in healthcare, finance, or any other regulated industry, this stuff isn't just "nice to have". It's make-or-break.

Responsible AI doesn't just work. It works accountably. That means bias testing, model documentation (like model cards), and human-in-the-loop processes baked in from day one. These aren't just technical niceties. They're business-critical safeguards. Being able to explain how a model reaches a decision is part of the same standard, which we cover in why AI transparency is no longer optional.

What UK data law means for your AI supplier

For UK businesses, the rules changed recently. The Information Commissioner's Office (ICO) confirms that all data protection provisions in the Data (Use and Access) Act 2025 are now in force. One change matters directly for AI: organisations can now rely on the full range of lawful bases when they use personal information to make significant automated decisions about people, as long as the required safeguards are in place. The ICO consulted on updated guidance on automated decision-making between March and May 2026.

A good AI development company should raise this without being asked. They'll ask early about the nature of your data, which regulations apply, and how it should be stored, accessed or anonymised. If you take card payments, that includes PCI DSS. Look for a security-first approach, with practices like encrypted storage, regular penetration testing, secure cloud infrastructure and clear access controls.

A standout sign is a certification such as ISO 27001 that they can point to as part of their workflow. Imobisoft has completed its third consecutive year of ISO 27001 accreditation.

Who owns the code, the model and the data?

This is the question buyers most often forget to ask, and the hardest one to fix after the contract is signed. Before you commit, agree in writing:

• Who owns the source code, and whether you have access to it throughout the project, not just at the end.
• Who owns the trained model, its settings and the prompts or evaluation sets built for your project.
• That your data stays yours, and whether it can be used to train anything for other clients.
• What documentation you will receive, and what a handover to another supplier or your own team would involve.

Ownership is also about which AI providers your system depends on. A system tied to one model provider can't take advantage of cheaper or better models without a rebuild, which we explain in our piece on AI vendor lock-in. If you are still deciding whether to build at all, our comparison of off-the-shelf vs custom software sets out where each makes sense.

The right AI partner works with you, not just for you

AI doesn't end at deployment. Models break. Users do unexpected things. The business shifts. That's normal.

The problem? Too many vendors treat the "go-live" moment like the finish line. They hand off the model, disappear, and leave you to figure out what happens when it starts making bad calls, or stops working entirely.

That's not partnership. That's outsourcing risk.

A real AI partner sticks around. They test in the real world. They adapt. They bring your domain experts into the loop early because, without their input, the model's just guessing.

Ask yourself:

• Are they collaborating with your teams or just running their roadmap?
• Do they have a process for learning from live use and improving the system over time?

If your AI service provider isn't building feedback loops and long-term support into the engagement, they're not thinking seriously about your success, or theirs.

What should happen after launch?

One common blind spot is model drift: the gradual decline in performance as real-world data shifts away from the conditions the model was trained on. A serious AI partner will bring this up early, not just at the end of a project. They'll talk about ongoing evaluation, performance metrics and feedback loops, and help you set up processes for retraining and refining models, so the system stays relevant and valuable over time.

Ask about running costs too. Model usage is often billed per use, so the cost of an AI system can change after launch as volumes grow, a point we cover in the LLM pricing trap. If a partner doesn't raise how your AI will be sustained and adapted, that's a signal they may not be equipped for serious delivery. Our piece on why AI implementations fail covers what goes wrong when nobody owns this stage.

Don't underestimate culture fit: it's the glue that holds every project together

You can have the most technically brilliant AI partner in the world, but if they can't communicate, can't collaborate, or just don't "get" your business, the project will stall fast.

Culture fit isn't soft stuff. It's what determines whether the relationship feels like a true partnership or just another transaction.

Here's what to look for:

• Do they speak your language, not just technically, but in terms of your industry, goals, and pressure points?
• Are they proactive with updates, or do you have to chase them for progress reports?
• How do they handle feedback? Do they listen or constantly defend?
• Are they building something with you, or just trying to finish a sprint and move on?

Technology can be learned. Culture can't be faked. A mismatch here will cost you time, trust, and traction.

How they communicate and price the work

Expect structured communication: sprint updates, regular demos, and honest conversations about risks or roadblocks. Mature partners give you a realistic view of what's possible and how long it will take. They'll walk you through project phases, outline delivery milestones and share budget assumptions upfront. And if something shifts mid-project, they'll flag it early.

One good way to test this: during early conversations, ask to see how they manage their work, whether that's a sample roadmap, a shared project board or how often they will report to you during delivery. Ask too how they price the work. A short, paid discovery phase before a full quote is a sensible way to test both the idea and the team.

Red flags that should end the conversation

• Only demos, no live systemsThey can show a prototype but can't name a system that is running in live use.
• Promises of instant resultsThey downplay how complex AI implementation is, or commit to timelines before seeing your data.
• Agreement to everythingThey never push back, question your assumptions or tell you when something doesn't need AI.
• Vague pricingNo phases, no milestones and no budget assumptions you can check.
• Silence on data and ownershipThey don't raise data protection, and can't say clearly who owns the code and the model.
• No plan after launchGo-live is treated as the end of the engagement.

Questions to ask an AI development company before you sign

Use these questions to compare suppliers on the same terms. A strong team will answer them with specific examples, not general claims.

1. What problem do you think we are solving, and does it need AI at all?
2. Can you show us a system you built that is running in live use, and who uses it?
3. What results did it deliver, and can we speak to that client?
4. How will you check that our data is good enough before you build?
5. How do you test for bias, and how will you explain the model's decisions?
6. How will you meet UK GDPR and the Data (Use and Access) Act 2025 for our data?
7. Who will own the code, the trained model and the data, and how would a handover work?
8. How do you price the work, and what happens if the scope changes?
9. How will you monitor, evaluate and retrain the system after launch, and what will it cost to run?
10. How often will we hear from you, and who will we be working with day to day?

Should you hire AI developers in-house or use an AI development company?

If you already have experienced AI engineers with time to spare, you may not need an outside team for every project. Hiring takes time. An AI development company makes sense when you need to move before you can hire, need experience your team doesn't have yet, or want to test an idea before committing to permanent roles.

The two can work together. A good partner will document the work and plan a handover so your own team can take over in time, which is one more reason to settle ownership at the start. For the wider case for choosing a partner carefully, read why choosing the right technology partner is crucial in the fast-changing world of AI.

Frequently asked questions

Sources

• UK Artificial Intelligence Sector Study (2025), research report for the UK government, covering calendar year 2025. The report describes its figures as experimental estimates.
• The Data (Use and Access) Act 2025: what does it mean for organisations?, Information Commissioner's Office, updated 19 June 2026
• ICO consultation on the draft guidance about automated decision-making, including profiling, Information Commissioner's Office, 31 March to 29 May 2026