How much power a grid produces tomorrow, what a mortgage costs, how much food a shop orders and whether a new law is kept all depend on an expectation of what comes next. Most of those expectations are never checked against what actually happened. We believe careful, tested models can make many of these decisions better.
Prices, budgets, power grids and public policy are all set on a view of the future. The quality of that view shows up later as waste, cost or missed targets, often long after anyone can trace it back.
We state what a model expects, with a range, before the outcome is known, and we keep the record. A model that is never scored against what happened has not earned anyone's trust.
A slightly better demand forecast means less food thrown away. A slightly better wind forecast means less backup power burned. Repeated across thousands of decisions, small improvements change how well things work for the people who depend on them.
Forecast bands, market regimes and stop rules, tested where feedback is fastest.
Early reads on regional activity, prices and housing, before the official figures arrive.
Demand for food and public services, and how fast an outbreak is growing.
Whether a law or policy actually changed what it set out to change.
Warming trends, biodiversity and the adaptation choices that follow from them.
Power demand, wind and solar output, and the price spikes in between.
Most ideas fail at the second gate. That is the point of having it.
Every project starts from a problem someone actually has. We write it down as a question the data can answer, with the people who know the problem best.
Out-of-sample tests against a fixed baseline. An approach that only works on the data it was built on is dropped.
New applications start as a small, time-limited pilot, with clear limits on what they decide on their own.
Drift checks on every input and output. When behaviour changes, the application does less on its own until we know why.
We start where the benefit is easy to name: cleaner and cheaper energy, less waste, fairer prices, better public policy and climate adaptation.
We favour partners with a problem worth solving and no data team of their own over those who could build it anyway.
Forecasts only make sense where good data exists and domain experts can check the assumptions and the results.