How to Choose an AI Development Company
Choose on proof, not on the pitch: a similar system already in production with real users, an interview with the engineer who will build yours, a fixed price on a closed scope, and a contract that leaves the code and the IP with you. For a complex project across several systems, score the shortlist on a weighted scorecard and let a paid proof of concept decide between the finalists. Unlocking Tech is a Portuguese software and AI engineering company based in Lisbon that takes a digital product from the diagnostic to production with the same team: scope, design, development, launch and evolution. Scope and price are fixed before production code, and most of our projects land between €15k and €50k.
Searching for an "AI development company" returns dozens of listicles of the "best" vendors — and almost none of them help you decide. This article is the opposite: a guide to choosing well, with the questions that separate companies that build real software from ones that make demos, the red flags to avoid, and what should stay yours at the end. It's written from this side of the table, with candour about what actually matters. Disclosure: Unlocking Tech, which publishes this blog, is an AI development company and competes for the projects this guide is about; the scorecard further down is the one we'd want a buyer to use on us.
What separates "AI" software development from a demo?
It comes down to the part that doesn't show up in the pitch. Building an AI feature that impresses in a meeting is relatively easy today; building AI software a company trusts with every customer, every day, is engineering. The difference is four things: real-world handling (dirty inputs, APIs that fail), measurement (knowing whether it's getting better or worse), escalation (knowing when to hand off to a person) and controlled cost. The AI model is maybe 20% of the work; the rest is what makes the software reliable. We covered the five places prototypes break in why your AI agent isn't reliable enough to scale.
The seven questions to ask any vendor
Take this list to the first meeting. The answers tell you more than any portfolio:
- Who owns the code at the end? The right answer is "you." If you end up dependent on a vendor's closed platform, you didn't buy software — you rented it.
- Who will I interview? You should be able to talk to the engineer who'll build it, not just a salesperson.
- What happens when it fails in production? If there's no clear answer on errors, alerts and recovery, there's no production — there's a prototype.
- How will I see progress? It should be visible in the tools (tasks, per-sprint demos), not in status reports.
- How do you measure the outcome? There has to be a business metric agreed before anything starts.
- Do you work in my timezone? A European, English-speaking team on UK time (Portugal), one hour behind Central Europe, is a real advantage over distant suppliers — same-day meetings, not next-day.
- What would you advise me not to build? A good partner tells you when something isn't worth it, or when buying beats building. Anyone who only says "yes" is selling, not advising.
Red flags
- Quotes a price before understanding the problem. A number before the diagnostic is a guess — or bait.
- Talks tech before your business. Whoever starts with the tool will sell you the tool.
- Shows demos, not systems in production. Ask to see something running with real users.
- Disguised lock-in. If leaving the vendor means rebuilding everything, the software was never yours.
If what you are buying is a customer service or lead qualification agent rather than a development team, use the scorecard, knock-outs and RFP questions in how to compare AI agent vendors.
How do you compare AI development vendors for a complex enterprise project?
Compare them on the same specification and the same test cases, not on presentations. First check what has to be in the proposal before it earns any points, then score the team that will actually work on the project on a weighted scorecard, and for the finalists run a paid proof of concept with acceptance criteria written before it starts.
A project is complex when it touches several systems and several teams, handles personal data, prepares or makes decisions about people, or falls into the high-risk class or carries transparency obligations under the EU AI Act. That is the usual shape of enterprise AI development in mid-sized and large organisations, and it's where a polished demo tells you least. What decides the outcome is how the AI development partner handles your data, your systems and the months after launch.
What has to be in the proposal before you score it. Five knock-outs. A vendor that won't put them in writing is out, whatever the price:
- Code, repository and IP in your company's name from day one, by contract.
- The AI Act risk class of the use case, and the roles in writing: who is the provider and who is the deployer under the EU AI Act.
- A processor agreement under GDPR Article 28 that lists the model provider as a sub-processor.
- Acceptance criteria and a test set written before production code.
- An exit plan: documentation, a runbook and a handover with no hidden cost.
The scorecard. Every vendor that passes the knock-outs gets a rating from 1 to 5 on each criterion:
| Criterion | Weight | Evidence to ask for |
|---|---|---|
| Team that writes the code (seniority, who programs, continuity) | 20 | Names and roles of the engineers on your project, and an interview with the one who leads it |
| Verifiable systems in production | 15 | A system in real use that you can check, with a reference contact |
| AI quality evaluation (test set, regressions when the model or the prompt changes) | 15 | The evaluation set and the results before and after a change |
| Integration with existing systems and data | 15 | A similar integration already running, and a look at your APIs before the quote |
| Scope and acceptance criteria closed before production code | 10 | A written scope with what is left out, and the acceptance tests |
| Operation after launch (monitoring, cost, who responds) | 10 | Runbook, alerts, response times and the running cost over 12 months |
| Ownership, exit and commercial terms | 10 | Contract clauses on IP, data export, warranty and change requests |
| Security, GDPR and AI Act beyond the knock-out minimum | 5 | What they do above the five knock-outs: access control, logs, human approval |
| Total | 100 |
The scale, the same for every criterion: 1 no evidence, only a promise · 2 evidence from a demo or a slide · 3 evidence for the normal case, gaps on the exceptions · 4 evidence you checked yourself, with minor gaps · 5 evidence you checked yourself, and the commitment written into the proposal.
The maths: Score (0–100) = Σ (weight × rating) ÷ 5. A 4 on every criterion gives 80. The weights are our recommendation, not a market standard: change them to fit your project, but do it before the proposals arrive — afterwards, everyone nudges them towards the vendor they already like. When two finalists are a few points apart, don't add another round of presentations. Pay both for a short proof of concept on the same test cases, with the acceptance criteria written before it starts, and let the result decide.
What criteria should you use to evaluate a custom AI development team?
Evaluate the team that will write the code, not the people who make the sale: the seniority of whoever programs, how they measure the AI's quality before and after every change, who runs it in production, and systems in real use that you can verify. Interview the engineer. At Unlocking Tech, a senior-only core of 12 engineers at principal and staff level writes the code, and the code, the repository and the IP are yours from day one.
- Who writes the code. Ask for the roles and seniority of the people on your project, by name. A team where seniors sell and juniors build looks the same in the pitch and very different three months in.
- How they measure the AI's quality. A fixed test set built from your real cases, run every time the model, the prompt or the data changes, with the results shown to you. "We test it by hand" means your users find the regressions first.
- Who runs it after launch. Monitoring, the monthly cost at your volume, model upgrades and who answers when something breaks. An AI implementation partner whose job ends at go-live leaves you with the hardest part; what that work involves is on our production support page.
- Continuity of the people. Ask what happens when an engineer leaves, and how much of the context lives in documentation and runbooks rather than in someone's head.
How this looks on our side, so you can hold us to it: the team you interview is the team that builds, and their names go in the contract. We answer RFPs and take part in paid comparative proofs of concept. Beyond the code, the repository and the IP, the keys and the hosting accounts are in your company's name from the first day. Our target is a first version live in 9 weeks — the median across our projects from diagnostic to first ship.
Why nearshore Europe (and not distant offshore)
The strongest setup for a European company brings three things that rarely come together: senior engineers, English by default, and a European timezone (Portugal is on UK time, one hour behind Central Europe). It means working with a team that's in the same meeting at the same hour, writes code to standards, and understands the European regulatory context — without the cost and friction of coordinating across eight-hour gaps. It's the basis of the model we describe in nearshore in Portugal, and the trade-offs are laid out in nearshore vs offshore software development.
The engagement models (and which to choose)
There are three ways to work with a development company, and the choice depends on how much of the delivery you want to control:
| Model | When it makes sense | Who manages |
|---|---|---|
| Staff augmentation | You're missing a specific profile | You |
| Dedicated team | You're missing a whole squad | Jointly |
| End-to-end development | You want a result delivered | The vendor |
The cost trade-offs between hiring and outsourcing are in in-house vs outsourced AI development, and how to size a build is in software development cost estimation. For projects where AI is the core of the product — not an add-on — you're buying custom AI development services, which almost always start with an AI strategy diagnostic to choose what to build first. If you want the broader picture of buying development as an ongoing service, see software development as a service.
How we work
Unlocking Tech is a Portuguese software and AI engineering company based in Lisbon that takes a digital product from the diagnostic to production with the same team: scope, design, development, launch and evolution. We work with mid-sized and large organisations, usually on projects that span several systems.
To be consistent with what we argue above: we start with a 30-minute technical diagnostic with a principal engineer — not a salesperson. You leave with a clear sense of what's worth building, in what order, and at what risk. The code and the knowledge stay yours, with no lock-in. And the software ships with reliability included from day one: error handling, measurement and escalation, not bolted on later.
If you're still working out what to build, 40 AI project examples is the best place to start.
Checked on 10 November 2026. If a legal reference or a cited source has changed, write to us through the contact page and we'll correct it.
Frequently asked questions
How do you choose an AI development company to build a custom product?
On proof, and on what stays with you after delivery. Ask for a similar AI project already in production with real users. Ask too for the metric and the evaluation set they will use to measure yours, an estimate of the monthly running cost at your volume, and the price of ongoing support. Confirm in writing that the code, the data and the prompts stay yours. For a project that spans several systems, score the shortlist on the weighted scorecard above and let a paid proof of concept decide between the finalists.
How much does it cost to develop AI software?
It depends on scope, but the right way to find out isn't to ask for a price list — it's to run a diagnostic that scopes the first project. A well-defined pilot comes in at a fixed price and in a few weeks; from there you measure ROI before scaling. Be wary of anyone who gives a number before understanding the problem. As a reference, not a quote: most of our projects land between €15k and €50k, at a fixed price agreed before production code — the number for yours comes after the diagnostic.
Is custom AI development worth it for a small company?
It's worth it when the problem has volume and the gains are measurable. For a mid-size company, the most expensive mistake is building too much too early — so the right sequence is one project at a time, reliable, measured, before the next.
Will I be locked into the vendor after the project?
You shouldn't be, and it's the most important question to ask. The code, documentation and knowledge should stay yours, with no lock-in to a closed platform. A serious company works so your team can maintain and evolve what was built.
What's the difference between an AI development company and an automation company?
They overlap, but the scale differs. Automation connects and orchestrates systems that already exist; AI software development builds the new product or feature. Many projects start in automation and grow into custom development when the value justifies it.