// 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.

Weekly
Retrained
Shadow
Deploy default
Multi
Horizon forecasts
Bounded
Confidence

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.

The capability surface.

Demand forecasting

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

Equipment failure prediction

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

Dwell-time optimization

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

Anomaly & drift

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

Optimization with constraints

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

Continuous retraining

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

Forecasts that retrain themselves, with confidence intervals operators can act on. Optimization that respects what your operation actually allows.

What this delivers.

  • 01
    Unplanned downtime cut 30–50% via predictive maintenance
  • 02
    Retail forecasting + replenishment AI lifts in-stock by 1–3%
  • 03
    Dwell-time AI cuts SLA misses 42% on logistics deployments
  • 04
    Anomaly detection surfaces deviations under 60 seconds
  • 05
    Shadow-deploy default keeps production accuracy stable through model updates

See Predictive Analytics running in your environment.

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