Predict delay risk before it happens — enabling proactive decisions across operations.
Measurable impact on operational efficiency
Delay risk flagged before departure windows
Ops teams get actionable signals, not raw data
Improved control over rotation knock-on delays
Built for growth across routes and airports
See the transformation from reactive to proactive operations
In high-frequency airline operations, small disruptions compound quickly. Weather shifts, congestion, rotations, and turnaround constraints can trigger cascading delays across the network.
With AI-powered prediction, operations teams get actionable insights before delays cascade, enabling proactive decision-making.
Built by Metosys — We designed a production-grade ML platform that learns from historical operations and live signals to forecast delay probability and expected delay minutes.
risk + minutes
top drivers surfaced
training + deployment
architecture
Simple, automated, and powerful
ops + weather + constraints
lags, route patterns, congestion, utilization
delay risk + minutes
scores + reasons + recommended focus areas
Designed for scalability, reliability, and secure operations across airline systems.
Real results from real operations
before delays compound
for rotations and resourcing
by reducing surprise events
through improved predictability
Delivered as a scalable ML system aligned with airline operations workflows.
We build production AI platforms that turn operational data into early-warning decisions.
NDA-friendly. We can anonymize, integrate, or deploy in your cloud.
Everything you need to know
Yes — the system is designed to start with available signals and improve over iterations.
Both. Real-time for immediate decisions, batch for planning and reporting.
IAM-based access, secrets management, and controlled data boundaries.
Yes — outputs can include top drivers for operational interpretation.
Explore more success stories from our portfolio
Built by Metosys — AI Engineering for Real Operations