Software, Data & AI for Airlines

What software problems do airlines most often need solved?

Most airlines run a core of large vendor systems — reservations, departure control, crew, maintenance — surrounded by dozens of smaller tools, exports, and spreadsheets that fill the gaps between them. The gaps are where time is lost: an operations controller re-keying data between screens, a network analyst assembling competitor schedules by hand, a customer team learning about a delay after the passengers do.

AeroCraft works in those gaps. We do not replace the core systems; we connect them, add the data they lack, and build the specific tools your teams need on top.

What does AeroCraft build for airlines?

• Operational data platforms that unify schedules, live positions, delays, and NOTAMs on IATA and ICAO identifiers
• Machine-learning delay and disruption prediction for your own rotations and connections
• AI agents that watch inbound aircraft, flag at-risk connections, and draft passenger communications for approval
• Network and competitor analytics built on schedule and route data
• Internal web tools for operations control, station management, and reporting
• Integrations with GDS platforms such as Amadeus and Sabre and with your existing vendor systems

Do legacy, low-cost, and charter airlines need different things?

Yes. Legacy carriers usually have the most integration debt: many systems, many owners, and a premium on not disrupting operations, so we favour incremental, run-in-parallel delivery. Low-cost carriers optimise relentlessly for aircraft utilisation and turnaround time, so delay prediction and fast operational tooling pay back quickest. Charter operators often lack in-house engineering entirely and need a partner who can deliver a complete, maintainable system — scheduling, customer communication, and reporting — at a sensible scale.

How does AeroCraft prove it understands airline data?

By running it. Skyinfo, our flight intelligence platform, tracks more than 500 airlines across 7,883 airports and 233 countries with machine-learning delay prediction and 24/7 live data. Our 16-endpoint API suite exposes the same schedules, routes, tracking, and NOTAM data. The data problems an airline faces — codeshares, schedule changes, conflicting feeds — are ones we handle in production every day.

See also: Route & network analytics · AI agents for operations

What results should an airline expect from a first engagement?

A first engagement should produce one measurable change in how a team works. Examples include at-risk connections flagged earlier than the current process allows, a weekly report that no longer has to be assembled by hand, or a competitor schedule change visible on the day it is filed rather than weeks later.

We agree the measure before work starts and report against it at the end, so the case for a second phase is made with evidence rather than enthusiasm.

Typical measures are hours saved per week in a named team, minutes of earlier warning on disruption, or the number of manual data steps removed from a recurring process. Each is something the airline can verify independently, without relying on our own reporting.