Artificial intelligence that solves
The first step is finding where AI gives you a real edge, whether that is predicting, classifying or automating, and putting it inside your product or your process with a result you can measure. No hype and no projects that never end.
AI is not the goal. Solving your problem is.
These days almost everyone wants to do something with artificial intelligence. And that is the trap: pointed in the wrong direction, AI becomes a hole the budget drains through. Months of experiments, a beautiful model trapped in a notebook and zero impact on the business. The challenge is never the technology, it is getting right where to apply it.
What changes things is rarely the most sophisticated model, it is having picked the right problem. A prediction that keeps the plant from stopping. A classification that saves hours of review. A heavy task your team stops dragging around. AI with a return you can point at.
So we start with the problem, not the algorithm. We look at whether AI helps or not, get the data in order and take the model to production. And when a decision has to be made on the spot, we put it inside the device itself. AI that is applied, integrated and measurable.
AI that goes into your product, not a slide deck
From data to a model running in production. These are the capabilities we put to work.
Custom models
We train them for your case, with your data and your metrics. Off-the-shelf solutions almost never fit quite right.
Edge AI
The intelligence runs on the device itself: it responds faster, needs no connection and the data never leaves.
Prediction
We anticipate a failure, a demand spike or a behavior so you can react before it happens.
Vision and recognition
Cameras that inspect, count and recognize at a pace no person can keep up for a whole shift.
Smart automation
We take the repetitive work off your team's plate so they spend their hours on what actually needs a brain.
Integration and MLOps
We put it into production and look after it afterward: monitored, updated and retrained when reality shifts.
A method that avoids the never-ending AI project
Five steps to go from idea to a working model, measuring risk at each one.
The case
What the problem is, what solving it is worth and how we will know it worked.
The data
We look at what you have, what state it is in and what still needs capturing.
A test
We validate the idea with a small pilot before spending too much.
Production
We integrate it into your product or process, in the cloud or at the edge.
Follow-up
We monitor it and keep tuning it as new cases come up.
AI with its feet on the ground and its eyes on the business
The difference between an experiment and a product is almost always the integration. Fitting a model into a device with little memory, wiring it into the systems you already run and keeping it working when reality shifts is the part that genuinely takes work. And it is exactly the part we tend to get right.
Because we also build the hardware and the software, we can take the intelligence all the way to the last corner: a camera that checks on the line, a sensor that warns before something fails, an assistant that understands what you ask it. We work from Barcelona, Madrid, London and Los Angeles.
We aim at the return
We pick cases that move a number, not demos to show off.
From data to device
We build our own hardware and software, so AI reaches all the way to the edge.
Private by design
With Edge AI the data is processed where it is born and never wanders off.
We do not leave you alone
The model is not a deliverable we abandon. We maintain it.
AI for every sector
AI looks different in each sector, but the way to apply it sensibly is the same across all of them.
What we get asked most
Do I need a lot of data to get started with AI?
Not always, it depends on the problem. The first step is seeing what data you already generate: sometimes that is enough, and sometimes you need to start capturing it properly. We tell you early, without the runaround.
What is Edge AI and when does it make sense for me?
It means running the model on the device itself instead of in the cloud. It pays off when you need an immediate response, when the connection is unreliable, or when you handle data you would rather not move around.
How do I know whether AI will actually help me?
With a short feasibility phase. We frame the case, estimate the return and test it with a pilot before investing fully. That way you clear up the doubts in weeks, not months.
Is this going to replace my team?
That is not the idea. We use it to clear the repetitive work out of the way and to give your people better data to decide with. People do what they add value to; the machine does the boring part.
Can you put it into a product I already have?
Yes. We connect it to your systems or integrate it into your own hardware, something not everyone can offer because we also do the electronics.
How long does a project like this take?
Feasibility takes weeks. Going to production depends on scope. We work in stages so you see results early and we avoid the project that drags on forever.
And when reality shifts and the model goes stale?
We retrain it. A model needs maintenance like anything else that is up and running, and that part is on us.
Where could AI help you?
Tell us your challenge and we will tell you honestly whether artificial intelligence helps you, how we would apply it and what to expect in return. We reply fast.