Calibration is the product.

A prediction engine has one real promise: when it says 73%, those things happen 73% of the time, over hundreds of resolved events. Everything else is decoration.

Predictive is built on that promise. Every prediction is dated, resolvable, and scored against reality. The track record is public. When we're miscalibrated, we fix the model — not the chart.

Under the hood: Monte Carlo simulation with Sobol sequences, Bayesian inference against a causal graph of global economic and market data, regime-conditional edge weights, and Shapley attribution so every probability shift comes with a reason.

We won't claim more than we can verify. Features that return synthetic data get deleted. Models that don't beat baselines get retired. The only thing that ships is what survives resolution.