We’re building the AI copilot for airline ops controllers. Join the waitlist.
When storms hit, 30+ ops controllers scramble with 20-year-old software that displays problems but doesn’t solve them.
One delayed aircraft ripples across the network. A mechanical in Atlanta creates cancellations in Dallas, Chicago, and Miami within hours.
Controllers make phone calls, juggle spreadsheets, and rely on tribal knowledge. Recovery takes 3–6 hours while costs compound every minute.
Which 8 of 400 flights do you cancel? Which tails swap? Which crews reassign? The search space is astronomical — humans optimize locally, not globally.
DOT’s automatic cash refund rules and EU261 make poor IROPS handling dramatically more expensive. The cost of inaction is growing.
OpsClear doesn’t replace controllers. It gives them superpowers, handling the combinatorial complexity so humans focus on judgment calls.
Real-time flight positions, crew duty times, aircraft locations, weather forecasts, passenger connections, and airport capacity flow into a unified state model.
Our digital twin runs thousands of what-if scenarios in seconds — testing cancel/delay/swap/divert combinations against a physics-based model of your network.
AI agents evaluate every option across all constraints simultaneously: rotation continuity, crew legality, passenger rebooking, gate availability, and total cost.
Controllers see a ranked recovery plan with dollar impact per action. One-click approval. The system learns from every decision to get smarter over time.
$ opsclear replay --airport ATL --date 2026-01-01
Worst weather day detected: 2026-01-01
Flights: 330 | Cancellations: 50
Metric Actual Optimized
────────────────────────────────────────
OTP Rate 11.8% 11.9%
Mean Delay 85.6 min 51.5 min
Total Cost $13.8M $10.1M
✓ OpsClear saves $3.7M (27% reduction)
➜ Annualized: ~$187M/year (50 weather days)
Our discrete-event simulation engine models your entire operation: every tail, every rotation, every crew pairing, every weather minute. Then AI agents explore the solution space to find optimal recovery paths humans would never consider.
Four forces are converging to make this the right time to build the decision layer for airline ops.
Real-time aviation-scale constraint solving is now feasible. Combinatorial optimization + ML forecasting crossed the performance threshold in the last 2 years.
Climate-driven severe weather is increasing. FAA controller shortages create more flow control delays. Demand is at record levels.
DOT automatic cash refunds, EU261 expansion, and passenger rights legislation make poor IROPS handling dramatically more expensive.
Jeppesen (Boeing) is distracted. Sabre AirCentre is aging. Nobody has built the AI-native optimization layer. The market is waiting.
We’re building OpsClear with a small group of design partners. Sign up for early access and updates.