An algorithmic application that dynamically improves fleet scheduling by optimizing existing
flight schedules, while integrating live scheduling changes to reflect best possible route for an
The end result is significant reduction of "dead legs" and optimized resource allocation across the
Of significance for flight planning departments, operators can perform manual checks on their
run optimization on-demand, and lock in flight schedules, realizing immediate and auditable fleet
A machine learning application that takes into account both historical flights and external datasets
(such as event calendars) combining them to accurately predict peek demand period between clustered
airports of high activity. When used in conjunction with Fleet Optimization, operators can ensure
assets are available in high demand locations.
At present, Demand Prediction can accurately predict flights between city "airport clusters" with
over 70% accuracy. As more are incorporated and an increased amount of input streams are added,
accuracy will continue to increase.
Flight provisioning is wholesale booking tool that works in concert with Optimization and
This tool equips operators with real time information as to the profitability retaining or
potential flight. Correspondingly, operators can determine whether it is best to satisfy a flight
own fleet, or serve the customer more efficiently by outsourcing to a partner for share of revenue.
This tool helps operators create custom rule governing the profitability of their own fleets,
operators to "live price" flights based upon operating cost and real-time demand signals. Much like
"Uber Surge Pricing", specific events and demand spikes will allow operators to maximize revenue
when demands peaks.