Artificial intelligence
We build AI into the systems your business already runs on.
Our AI work covers workflow automation, custom AI tools, and agents that take on the repetitive, manual parts of how your business operates. We build these into your existing systems, connecting the data and processes you already have, so the technology does real work instead of sitting in a demo. Every build is shaped around how your organization actually runs.
We also work with teams that know AI matters but aren’t sure where it fits. That starts with an honest look at where automation would genuinely save time, where it wouldn’t, and what can be built reliably given your data and your constraints. Whether you want to add AI to an existing process or build something new around it, we build for reliability and for the regulated environments many of our clients work in. The goal is technology you can put into daily use and then stop thinking about.
If you’re trying to work out where AI actually fits in your business, let’s talk.
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Sovereign AI
AI that runs inside an environment you control, where your data stays within your own systems and borders instead of being handed to a third party. For organizations working under strict data and compliance rules, this is often what makes AI usable at all. We build models and tooling that you host, govern, and stay accountable for.
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RAG (retrieval-augmented generation)
A way to ground an AI model in your own documents, data, and knowledge, so its answers come from your business rather than a generic model trained on the open internet. We build retrieval systems that give your teams accurate, source-backed answers from the information you already hold, instead of confident guesses.
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MCP (Model Context Protocol)
A standard for connecting AI to your tools, systems, and data in a controlled, consistent way. It is what lets an AI model do more than chat: read from the systems you use, act inside them, and stay within the boundaries you set. We use it to plug AI into your existing stack without rebuilding everything around it.
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Agentic AI
Systems that don’t just answer a prompt but carry out multi-step work on their own, automating processes from start to finish. We build agents that handle real tasks inside your operation, with the oversight and controls needed to trust them with the work.
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AI findability (GEO)
Being found and cited when people put their question to ChatGPT, Claude, or Google’s AI answers instead of a search box. It is largely the same work as good SEO: clear content that answers real questions, a logical structure, structured data, and a plain summary of what you do that a model can read without guessing. It is part of our SEO and GEO services.
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AI accessibility
Personal AI agents no longer only read a website. They come to do something on it: request a quote, book an appointment, place an order. We test whether an agent can complete those tasks on your site, list what stops it, and fix it in the build. Much of it is the same work as accessibility for people: readable labels, clear errors, and no steps that exist only on screen.
Read more: GEO got you found. But now an AI agent wants to use your site.
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Consulting
Clear, honest advice on where AI fits in your business and where it doesn’t. We help you find the use cases worth pursuing, avoid the ones that won’t pay off, and shape a plan that fits your data, your team, and the rules you operate under. Whether you’re starting fresh or adding AI to existing processes, you get a partner who has built this, not just talked about it.
To AI or not to AI
Not every problem needs AI, and we’ll tell you when it doesn’t. We start by looking at how your business actually works, then figure out where automation earns its place and where it doesn’t. From there we build the tooling that fits: systems that connect your data, take repetitive work off your team, and hold up under daily use. The point isn’t AI for its own sake. It’s work that runs faster, and results you can rely on.
Our AI process
How we take an AI project from a first question to daily use.
Discovery and scoping
Meet us in our Brussels or New York office, or we’ll come to you. We start with how the work is done today: who does what, in which systems, and where time gets lost. From there we pick the use cases where AI earns its place, and we say so when a simpler fix will do.
- Kickoff meeting
- Process and data mapping
- Use case selection
- Privacy and AI Act check
- Success criteria
- Timeline planning
Design and build
We design the system around your own sources and tools. The AI answers from your documents and data instead of the open internet, and it connects to the systems you already use through APIs or MCP. We choose the model per project, based on the task, the cost, and where your data is allowed to go.
- Solution architecture
- Knowledge base from your documents, websites, and APIs
- Integrations (API, MCP)
- Model selection and PII protection
- Instructions and guardrails
- Interface: chat, widget, or inside your own tools
Testing and launch
An AI system is tested differently from a website. We run it against realistic questions and tasks from your own practice, and score the answers on correctness, tone, completeness, and use of sources. It goes live once the results hold up, starting with a small group where that makes sense.
- Test sets with real questions
- Scored evaluation rounds
- Privacy and security checks
- Pilot with a limited group
- Go-live
- Conversation logging from day one
Operation and follow-up
Launch is where the learning starts. We read what people ask, fill the gaps in the knowledge base, and test every change to the instructions before it goes live. Your team stays in control: you can see what the system was asked and what it answered.
- Conversation review
- Knowledge base updates
- Versioned instructions with a test module
- Documentation for your team
- Ongoing support
Building AI tools that connect your systems and take work off your team.
Not everything needs AI, and we’ll say so when it doesn’t. The tools we build connect your data, automate the steps that slow people down, and run reliably on their own, once we’ve mapped where they genuinely earn their place.
