A map of use cases
The processes where AI produces measurable savings, ranked by impact and difficulty. Including the ones to avoid, and why.
Applied enterprise AI
Artificial intelligence for business
You don't need to reinvent the company to use AI. We start from a process that costs you hours every week, test it on real data within weeks, and scale only once the numbers justify it.
30 minutes, no commitment. If the use case doesn't hold up, we'll tell you on the call.
- POC in a few weeks
- Fits the systems you already run
- GDPR and EU AI Act compliant
- Code and documentation stay yours
Who it's for
This is the right path if you recognise yourself here
AI pays off when there is a concrete process to lighten. These are the signals of a sensible project.
- There's a repetitive task burning hours of skilled work every week.
- The data you'd need already exists, but it's scattered across your ERP, documents, email and spreadsheets.
- You've seen convincing demos, but nobody has shown you how they hold up on your data.
- You want to know whether AI is worth it before committing budget and people.
If you're looking for help with marketing campaigns or content generation, we're not the right partner: we build software systems.
What you get
Concrete artefacts, not a slide deck
At the end of a first engagement you hold material you can decide on — and that stays useful even if you stop there.
A POC running on your data
Not a demo on sample data: a working prototype on your real data, with its limits stated openly.
Success criteria agreed upfront
We define what "it worked" means before writing code, so the decision at the end is objective rather than a matter of impression.
Code, models and documentation
Everything we produce is yours, documented so another team could maintain it.
A path to production
What it takes to move from prototype to daily use: integrations, running costs, ownership, training for the people involved.
How we work
From first conversation to prototype, in weeks
A short, verifiable path designed to let you decide early and with evidence.
Understand the process
We look at how the work happens today and where time is lost. We talk to the people who actually run the process, not only to those who describe it.
Check the data
We verify that the required data exists, is accessible and is good enough. This is where many projects would stall later, at far greater cost.
Build the POC
We develop the prototype for the chosen use case and measure it against the agreed criteria. You see progress along the way, not just at the end.
Decide together
With numbers on the table: go to production, adjust course, or stop. Stopping is a legitimate outcome too — and it was cheap.
What we work on
The technologies we actually run in production
Not a catalogue: these are the areas where we build systems that stay switched on every day.
AI agents
Systems that complete tasks autonomously by orchestrating tools, steps and checks, with human oversight where it matters.
RAG and document search
Reliable answers grounded in your documents, with source citations: the most solid way to apply AI to company knowledge.
Predictive models
Forecasting, classification and anomaly detection built on the history you already hold.
Process automation
Manual work on documents, email and case handling run end to end, with exceptions routed to a person.
Integration with existing systems
Your ERP, CRM and databases stay where they are: AI plugs in on top, without forced migrations.
On-premise or cloud deployment
Containerised solutions that can run on your own infrastructure when data must not leave it.
Working together
How an engagement is structured
Three ways to work together, depending on how well defined the problem is.
Fixed-scope POC
One use case, with objectives and success criteria set before we start. The cheapest way to turn a hypothesis into a decision.
When AI hasn't been tried in the company yet.
Development project
From validation to a production system: integrations, error handling, monitoring and handover to your internal team.
When the POC said yes and it needs to become everyday.
Ongoing advisory
We support your internal team on technical decisions: architecture, vendor evaluation, review of work already underway.
When the skills exist but outside guidance is needed.
We don't publish rate cards because a quote depends on scope: the volume and quality of your data, the integrations required, the duration and the level of ongoing support. We reconstruct those together on the first call, and the proposal follows from them with explicit numbers.
FAQ
What companies ask us before starting
With the most boring process, not the most strategic one. A repetitive, well-bounded task validates quickly, produces a visible result and teaches the organisation how to work with these tools. Ambitious projects go better as a second step.
It depends on three things: the state of your data, how many integrations with existing systems are needed, and how long the system has to be supported after release. A POC on a bounded use case is a matter of weeks; a production system wired into your ERP is a different scale. On the first call we establish the scope and prepare a proposal with explicit numbers.
No, and virtually no company has it. You need data that is good enough for the chosen use case. Checking the data is one of the first steps precisely so problems surface while they're cheap to fix, rather than mid-project.
That's a useful outcome, and it's exactly why you start with a POC. You've spent a few weeks instead of an annual budget, and you know precisely why that use case doesn't hold — often it also reveals which process would work instead.
Not necessarily. Where confidentiality requires it, we design solutions that run entirely on your infrastructure, on-premise or in a private cloud. It's a decision to make early, because it shapes the architecture and which models can be used.
Related services
Different paths depending on what you need right now: to understand, to decide, or to build.
AI consulting
We help you decide what's worth doing with AI, what isn't, and in what order. Then, if it makes sense, we build it: we're the same people who write the code, so the advice has to survive contact with reality.
AI consultant
Sometimes you don't need to hand over a project — you need someone to think with before deciding. A senior engineer who knows your context and is consistently available, without hiring a full-time role.
AI software development
Most AI projects stop at the demo. The distance between a prototype that works in a meeting and a system that survives daily use is made of integrations, error handling and monitoring — that's the part we do.
Let's talk
Tell us which process you'd like to lighten
Half an hour to work out whether there's a solid use case, what it would take to validate it and how long that takes. If there isn't one, you'll hear it from us.