Technical Overview

What makes Fount different

Most modern forecasting systems, including advanced AI models, are designed to recognize patterns in historical data. They identify correlations between variables and use those patterns to predict future outcomes.

However, correlation does not explain causation.

Fount is built on causal modeling. It identifies which factors actually drive outcomes, rather than simply observing that they move together. This allows it to distinguish between meaningful drivers and coincidental patterns.

As a result, Fount can explain forecasts, not just generate them. It provides visibility into which variables are influencing results and the extent of their impact.

More importantly, it supports decision simulation. You can evaluate how changes in pricing, promotions, or media investment are likely to affect outcomes before taking action.

This makes Fount more reliable in real-world environments, where underlying conditions frequently change.

Benchmark results

Fount's accuracy has been independently validated on the M5 competition, the world's most rigorous forecasting benchmark. One single Fount model outperformed all major competitors:

A lower WMRSSE score indicates better forecast accuracy. The M5 competition winners improved benchmarks by 20–22%. Fount beat them.