Forecasts, predictions and early warnings built on your own data, and put where decisions are made: on the dashboard, in the app and in the control room.
Every organisation collects data. The value comes when it tells a person what is likely to happen next, early enough to do something about it.
Bookings, sensors, tickets and finance each sit in their own system, so nobody sees the whole picture.
Monthly reports explain what already happened. By the time they arrive, the moment to act has passed.
A promising model is built once, then never connected to a screen anyone uses, so nothing changes.
Most requests fall into one of four shapes. Many projects combine two: a forecast that feeds a plan, or an alert that opens a task.
What will happen next, and how sure we are, by the hour or the day.
For exampleArrivals per gate predicted for a sold-out match, so staff are in place before the rush.
Readings and patterns that drift from normal, flagged while there is still time.
For exampleA pump whose readings drift is serviced before it fails.
Groups of customers, visitors or assets that behave alike, so each gets the right response.
For exampleVisitors grouped by how they arrive and spend, to plan services around them.
What-if scenarios run in minutes, and the best plan for the people, routes and time you have.
For exampleCrowd scenarios for a venue tested in under a minute instead of three days.
The quickest returns come where a decision is made every day on instinct, and the data to make it better already exists.
Arrivals and occupancy predicted per gate and section, from tickets sold and past events.
Entry, exit and evacuation plans simulated quickly enough to test many options.
Maintenance reports and sensor readings read together to spot what needs attention.
Results, training and conditions joined, so coaches see why, not only what.
Bookings and occupancy forecast, so staff and stock match the guests who will come.
Signs that a customer is drifting away, flagged while there is time to keep them.
Payments and entries that do not fit the pattern, surfaced for a person to check.
We work back from the decision, not forward from the data, so every model ends up on a screen someone uses.
We start from the decision to improve, such as staffing a gate, ordering stock or servicing a pump, and the data that bears on it.
You getOne decision, one measure of success
Sources joined, cleaned and checked, with a pipeline that keeps them fresh rather than a one-off export.
You getData you can trust, kept up to date
Forecasting, classification or optimisation models, tested against what really happened before anyone relies on them.
You getA model proven on your history
Results shown where people already work, watched for drift and retrained as patterns change.
You getPredictions people act on
From the pipeline that feeds it to the screen that shows it, so a model does not stop at a report.
Widely used, open tools, so your models are easy to maintain and never locked to one vendor.
We have built models for crowds, facilities and live sport, and put them in front of the people who act on them.
Crowd forecasts and live tracking for world-scale events, where a late answer is no answer.
The same team builds the platform, so predictions appear in the app, the twin or the control room, not in a separate file.
Counting without identifying, and data kept on your servers wherever it can be.
for a crowd planning scenario that once took three days
faces recognised by our crowd headcount: it counts, it does not identify
data, models and results, on the cloud or servers you choose
AI assistants and agents that work in production: they read your data, act on events and run multi-step work, with a person approving what matters.
Explore→Private AIThe assistants and agents your team wants, running where your data already lives: your servers, your private cloud, under your control.
Explore→DevelopmentConnected devices, live digital twins and VR walkthroughs, built end to end, from the firmware on the chip to the command-centre view.
Explore→Often less than you think. Many useful models start from a year or two of the records you already keep, and improve as more arrives.
Data science finds patterns and makes predictions from your data. AI agents act on information and run tasks. They work well together: a forecast can tell an agent when to act.
Yes. We can build and run them on Google Cloud, Microsoft Azure or your own servers, so your data stays where you want it.
We test it against your own history before anyone relies on it, then keep measuring it in use and retrain it when patterns change.
It depends on the scope, and we agree it before any work starts. The first conversation is free.
Tell us where you want to be. We will show you the fastest way to get there, with the right mix of strategy, technology and AI.