// Platform · CapabilitiesForecasting · Anomaly · Drift

Forecasting and optimization, tuned to your baseline.

Demand forecasting, equipment-failure prediction, dwell-time optimization tuned to your operating model. Retrained weekly against ground truth.

// Why it matters

The problem

01
Stale forecasts. Models trained quarterly drift between retrains. By the time the next retrain ships, accuracy has decayed.
02
No confidence intervals. Point predictions delivered without confidence are unusable for operational decisions.
03
Generic baselines. Off-the-shelf models trained on cross-industry data underperform models tuned to your operating baseline.
04
Optimization without constraints. Models that propose theoretically optimal answers without respecting union rules, asset constraints, or contractual SLAs aren't deployable.

// What Innfini delivers

Multi-horizon retail, logistics, and operations forecasting. Tuned per customer baseline.

Vibration, temperature, pressure, flow, and acoustic signals fused with failure-mode models.

SLA-aware dwell-time prediction. Agents reroute or pre-stage before breach.

Continuous anomaly detection across sensor and operational data. Drift triggers retrain.

Route, slot, schedule and resource optimization respect contractual SLAs, asset constraints, and union rules.

Weekly retrain cadence against ground truth. Shadow-deploy before promotion.

// Measured impact

A 60-minute architecture review with our solutions team. We map your sensors, your systems, and the workflows where this capability moves the needle.