Forecasting has always been built one industry at a time

Look at how forecasting software got built and the same pattern shows up everywhere. Energy had its own vendors. Insurance had actuarial platforms. Retail had demand-planning suites. Finance had its quant stacks. Each one deep inside its own vertical, each one reinventing the same underlying idea: predict something, then try to keep the prediction accurate.

Why everything was vertical

For a long time that made sense. The data, the jargon, the regulations, and the buyers were all different, so the tools were built industry by industry. The hard, general problem underneath, a forecast that drifts as the world changes, got solved over and over in separate silos, by separate teams who rarely compared notes.

The problem underneath is the same

Strip away the domain and the core is identical. A demand forecast, a loss forecast, an energy price forecast, and a weather forecast all decay for one reason: they were fit to the past and the present keeps moving. The correction, watch against reality, learn the error, adjust, does not care what industry it is running in.

The AI era makes one layer possible

What changed is that modern AI can learn those error patterns from outcomes without being hand-built for each vertical. That is what lets a single prediction layer sit on top of forecasts in energy, finance, insurance, supply chain, or sales and improve them with the same underlying method. Industry-agnostic by design, because the thing it fixes is universal.

We started in weather because it is the purest test: forecasts everywhere, checked against reality constantly. Improving every major weather model we tested proved the layer works. Nothing about the method is weather-specific, and that is exactly the point. It is infrastructure for prediction, whatever you are predicting.