Flight Data & Software for Air Cargo and Logistics

Why does air cargo depend on accurate flight data?

A large share of air cargo travels in the bellies of passenger aircraft, so a forwarder's plan is only as good as its view of passenger schedules. When a flight is delayed, cancelled, or swapped to a smaller aircraft, shipments miss connections, customs slots, and onward trucking. Shippers increasingly expect to be told about that before it happens, not after.

Most logistics platforms still learn about disruption from carrier messages or manual checks. Live tracking and delay prediction make it possible to act hours earlier.

What can AeroCraft provide for cargo and logistics?

• Schedules APIs — current, historical, and future — normalised to IATA and ICAO identifiers, including equipment type
• Live flight tracking from ADS-B and satellite sources, including oceanic and remote airspace
• Delay status and ML delay prediction to flag at-risk shipments before departure
• Airline routes data showing which carriers serve which city pairs and how often
• MCP servers that let cargo AI assistants answer “where is my shipment’s flight?” from live data
• Custom platforms: shipment visibility portals, capacity planning, and exception management

How does delay prediction help a logistics business?

A delay prediction attached to each booked flight turns shipment monitoring from reactive to proactive. If a feeder flight is predicted to arrive too late for its connection, the platform can alert the operations team, suggest alternative routings using the airline routes data, and notify the customer — all before the delay is official. Predictions are trained on historical schedules and actual movements and adjusted using the live position of the inbound aircraft.

Can AI assistants in logistics use this data?

Yes. Logistics platforms are adding AI copilots for customer service and operations, and those assistants need trustworthy, current flight data to answer questions correctly. AeroCraft packages its aviation APIs as Model Context Protocol tools, so an assistant can look up flight status, predicted arrival, and alternative routings directly rather than guessing. The same data powers Skyinfo across more than 500 airlines and 7,883 airports.

See also: MCP servers for aviation data

What does a shipment-visibility integration typically involve?

The core is matching each booked shipment to the flights it travels on, using flight numbers, dates, and routing from your booking system. Once matched, every flight is monitored for status changes and predicted delays, and each shipment inherits the risk of its flights. Exceptions are raised when a predicted arrival breaks a connection or a delivery promise.

From there, the information can flow wherever it is useful: an operations exception queue, customer notifications, a self-service tracking page, or an AI assistant answering customer questions. Most clients start with the internal exception queue, then expose the data to customers once they trust it.

Because predictions are attached to flights rather than shipments, the same monitoring covers every shipment on a flight at once, which keeps processing efficient even at high volumes and makes the system straightforward to scale across lanes and stations.