F1805 · an Elite AI Empire product

Your model says 0.9.
Does it win 90% of the time?

Probably not. Raw model scores are confident, not calibrated. Empire Calibrate maps your scores to true probabilities — isotonic & Platt calibration, reliability diagrams, Brier / log-loss scoring, and drift alerts — so a 0.9 actually means 90%. One API, any model.

See pricing Join the waitlist

# Fit once on labeled outcomes, then calibrate every prediction. cal = calibrate.fit(scores=val_scores, outcomes=val_labels, method="auto") p = cal.transform(0.90) # → 0.71 (your "0.9" really means 71%) cal.brier() # 0.182 → 0.119 (lower = better) cal.reliability_curve() # exports the diagram for your dashboard cal.drift_check(live_batch) # ⚠ calibration drifting — refit recommended

Calibrate

Isotonic + Platt, auto-selected

Feed it a held-out set of scores and outcomes; it fits the calibration map (isotonic, Platt/sigmoid, or beta) that minimizes log-loss on your data and picks the best automatically. Then transform() every future score into a real probability.

Score

Brier, log-loss, ECE

Quantifies exactly how mis-calibrated your model was and how much Calibrate fixed it — Brier score, log-loss, and expected calibration error, before and after. Numbers you can put in a model card.

Visualize

Reliability diagrams

The reliability curve shows where your model is over- or under-confident across the probability range. Drop the rendered diagram straight into your monitoring dashboard.

Watch

Drift alerts

Calibration decays as the world shifts. Calibrate watches live batches and alerts when the mapping no longer holds — so you refit before a mis-priced 0.9 costs you a bad decision.

Why this matters: any decision that multiplies a probability by a payoff — risk sizing, fraud thresholds, ad bidding, churn intervention, medical triage scoring, betting — is only as good as the probability. A confident-but-wrong 0.9 silently breaks every downstream calculation. Empire Calibrate ships the calibration engine and scoring math; it learns from your labeled data and never carries our own thresholds, weights, or domain models.

Who it's for

ML teams shipping classifiers/scorers into decisions: risk & fraud, pricing & bidding, churn & lead scoring, forecasting, and anyone whose downstream math assumes the score is a probability.

Why not just sklearn?

CalibratedClassifierCV is great — if you wire it, validate it, monitor drift, and rebuild the reporting yourself. Calibrate is that, hosted, with reliability diagrams, scoring, and drift alerts as an API. See the comparison →

The pitch in one line

"A probability you can't trust is just a vibe. We make your 0.9 mean 90%."

Early-access waitlist

Public launch 2026. Early-access = founding pricing locked (20% off forever) + a free calibration of one model + priority onboarding.

Your model says 0.9.Does it win 90% of the time?