Traditional movement control has long meant tracking aircraft and supporting decisions during normal and disrupted operations

Air-OS predicts disruptions, models their ripple effects, recommends the most optimal strategies, and automatically aligns dispatchers, crews, and ATC

Capability for Network Resilience

Predictive airline network

and fleet management

at machine speed

Movement Control

Predict and pre-empt disruptions

Air-OS connects all data streams: ARINC 424, AMM, OIS, NAS, SWIM, ADS-B, and advanced weather data feeds, continuously forecasts demand-capacity gaps, weather-driven IROPs, and system bottlenecks. Operators see where disruption is forming and receive clear mitigation strategies in advance.

Platforms

Predict imbalances

before they become disruptions

Capabilities

AI-Enabled Congestion Prediction

Air-OS forecasts where and when airport and airspace imbalance will form before disruption materializes. By continuously modeling filed demand against traffic flow restrictions, ADS-B, weather, runway and taxiway availability, NOTAM-driven closures, and system advisories, it pinpoints emerging congestion across airports, procedures, and constrained flow paths.

Capabilities

Airline-Specific Impact Detection

Air-OS doesn’t just alert operators, it evaluates trade-offs, surfaces recommended departure adjustment or reroutes, and auto-coordinates the decision across stakeholders. Dispatchers, station managers, and ATC chiefs all operate from the same live picture.

Capabilities

Network-Aware Response

When disruptions hit, Air-OS simulates cascading impacts and recommends adaptive strategies to minimize cost, delay, and passenger disruption. Execution is instantly communicated to all roles, making the network self-recovering instead of reactive.

Highlights

What-If Simulations

Context-aware actions ranked by cost, safety, and efficiency.

Decision Recommendations

AI models surface emerging disruption from weather, ATC, crew, and fleet signals before operational thresholds are breached, giving teams the lead time to act rather than react.