Confidence-calibrated decisioning for automated systems. Choose actions under uncertainty. Explain every choice. Learn from outcomes.
Static rules with hardcoded thresholds. No confidence scoring, no uncertainty quantification. When conditions drift, the automation doesn't know it's wrong.
Actions happen. Incidents follow. Post-mortems stall because there's no audit trail, no explainability, no way to trace the decision back to evidence.
No feedback loop. No outcome tracking. The system makes the same mistakes on repeat because nothing connects decisions to results.
IDBE gates autonomy on calibration quality. The system starts conservative and earns deeper planning authority as its predictions prove reliable.
Fixed dependency graph. Execute known-good sequences. Zero exploration.
Contextual exploration. LinUCB + Thompson sampling pick the best arm given features.
Single-step lookahead. Evaluate action consequences before committing.
Multi-step MCTS planning. Full tree search with confidence-bounded rollouts.
Push context. Get a confidence-scored recommendation. Act on it. Report the outcome. Replay to recalibrate. Every decision is logged, traceable, and improvable.
LinUCB + Thompson Sampling. Contextual exploration with provable regret bounds.
CUSUM + KS tests. Catch distribution drift and concept shift before they cascade.
Kaplan-Meier + Weibull models. Predict time-to-event for SLA and reliability targets.
Topological sort + MCTS. From fixed DAGs to full tree search as trust increases.
ACI + Mondrian predictors. Distribution-free prediction intervals with coverage guarantees.
Calibration-gated daemon. Routes decisions through the trust curve, logs everything.
Full library, MIT licensed. Run it yourself.
Managed decisioning service with dashboards and replay.
On-prem deployment, custom modules, dedicated support.
We instrument your existing automation, measure calibration gaps, and show you exactly where confidence scoring changes outcomes.