Plenty of companies want to "add AI" without a clear picture of what that means in practice. The good news is that many valuable uses are modest: summarising documents, routing support tickets, extracting data from invoices or answering staff questions from an internal knowledge base. If you are looking into AI software development Europe has an extra layer of regulation to consider, so choosing the right partner matters even more.
Start with the problem, not the model
The strongest AI projects begin with a repetitive task that eats time and follows recognisable patterns. Before talking to any vendor, write down:
- The task you want to speed up or automate
- Who does it today and how long it takes
- What a good outcome looks like, and what a costly mistake looks like
- Where the data lives and who is allowed to see it
A partner who asks these questions before discussing models or tools is usually a partner worth keeping. You can see how we approach this on our AI and automation page.
European rules you cannot ignore
GDPR
If personal data is used to train, prompt or evaluate a system, GDPR applies. That means a lawful basis for processing, data minimisation, clear retention rules and the ability to honour access and deletion requests. Ask your partner where data is processed and whether any third-party model provider stores prompts.
The EU AI Act
The EU AI Act classifies systems by risk. Many business tools, such as internal search assistants, fall into lower-risk categories with transparency duties. Systems used in areas like hiring, credit or education can face much stricter obligations. Your development partner should help you understand which category your idea likely sits in and recommend getting legal advice where the stakes are high.
Data residency
Some organisations need data stored and processed within the EU. Confirm that the hosting setup and any external AI services can support that requirement before the build begins.
How AI projects are usually priced
AI work tends to have more uncertainty than a standard web project, because nobody knows how well a model will perform on your data until it is tested. That is why many providers propose a staged approach:
- A short discovery and feasibility phase to test the idea on real samples
- A proof of concept with clear success criteria
- A production build with integrations, monitoring and security
- Ongoing running costs and improvements
Costs are driven by data preparation, the number of systems to integrate, accuracy targets, human review steps and the usage fees of any model provider. Agree on whether invoices are in euros or another currency, and how variable usage costs will be passed on.
Questions to put to any vendor
- How will you measure whether the system is good enough to launch?
- What happens when the AI is unsure or wrong?
- Can we switch model providers later without rebuilding everything?
- Who owns prompts, fine-tuned models and evaluation data?
- How do you prevent sensitive data leaking into logs or prompts?
- What monitoring will run after launch?
Working across borders and time zones
European teams often span several countries and languages. Clarify which languages the system must handle, since performance can differ between them, and agree on meeting times that suit everyone. A short weekly demo keeps stakeholders aligned far better than long written reports.
FAQ
Do we need our own data scientists to start?
No. A capable partner can run the early phases. Over time it helps to have someone internal who owns the system and understands its limits.
Is it safe to use public AI models with company data?
It can be, with the right contracts, settings and architecture. Check data retention terms, use business-grade services and avoid sending unnecessary personal data.
How do we know if AI is the right tool at all?
Sometimes a simple rules-based automation does the job better and cheaper. A good discovery phase should tell you honestly.
Next step
If you have a process in mind and want a realistic view of what AI can do for it, Export Apps works remotely with European teams on discovery, prototypes and production systems. Reach out here to start the conversation.