Every figure below is bounded to the work that produced it. We publish the boundary with the number, because a number without one isn't a number, it's a mood.
Fuel distribution is a prediction problem wearing a truck. We built an integrated management system for the mobile workforce and the people who run it — mining fleet data, work metrics and production to close the gap between the best asset and the worst one.
Every ordering platform starts by asking the buyer to change: download this, make an account, learn our catalogue. Most never do — so the order arrives late, or wrong, or not at all. EasyOrder starts where they already are. A message, a link, done. The result is a shelf that matches its demand.
The seasonal plan is written in January. Then the weather moves, the consumer moves, the promotion lands — and €500k+ a year leaks out of the margin because the forecast can't follow. AgriSyncAI keeps matching supply to demand on a 7–21 day outlook, long after the plan is set.
Huginn collects. Munin mines. Five feeds come in — and not one of them is inside your company — and what comes out is what they mean together, which is never what any of them says alone.
Your people know why last March went wrong. No model has that. We start there, and it never stops being half the system.
The model is the part that fits on a slide. The other eleven boxes are the part that decides whether it survives Tuesday. A data scientist who only considers the small box builds something beautiful that nobody can run.
A diagram of the twelve components of a production machine learning system. Eleven of them — cloud infrastructure, data collection, data verification, feature extraction, configuration, machine resource management, monitoring, process management tools, analysis tools, serving infrastructure and user interface — surround one small central box labelled ML code, which occupies roughly three percent of the total area.
We are not trying to live inside your business. The whole method is built to make us unnecessary.
We find the signal in the data. Yours, the market's, and the noise between them. Before anyone proposes a solution, we establish what is actually true — which is usually not what the last report said.
We co-design the human/machine experience — with your people, not for them. The machine takes the work that machines are better at. Your team keeps the judgement. The line between the two is drawn deliberately, in the room, with the people who'll live on both sides of it.
We solve the problem with data-informed decisions — and then we hand you the keys. Independence is the deliverable. If you still need us in year three, we built it wrong.
In those economies data wasn't a strategy. It was the only competitive advantage available. Far from being bulletproof, our journey is full of scars — we learned to adapt faster and cheaper.
We are passionate about technology and how it can help extend economic opportunities to the populations who need them most. We are looking for partners and clients with whom we can work toward a more sustainable future.
One call. We'll tell you within thirty minutes whether we're the wrong people for it.