CargoAi has announced the launch of AI Predictive Tracking, a new capability designed to help air cargo stakeholders anticipate operational risks and shipment delays before they materialise.
The solution is available both within CargoMART and as an add-on to the CargoCONNECT Track & Trace API.
The Singapore-headquartered digital airfreight platform said this reflects a growing operational challenge in air cargo: while traditional tracking tools only flag issues once milestones are logged, often leaving little time to intervene when a shipment is at risk.
CargoAi noted that its Predictive Tracking introduces an additional layer of intelligence by forecasting upcoming shipment events and triggering early risk alerts.
AI Predictive Tracking uses machine learning models trained on millions of historical shipments, combined with live airline flight updates, to predict the expected timing of each key milestone in an air cargo journey. These include documentation submission (FWB), acceptance (RCS), manifesting (MAN), departure (DEP), arrival (ARR), freight availability (NFD) and final delivery (DLV).
Instead of relying solely on reported events, the system generates probability-based predictions, including median (P50) and conservative (P90) estimates.
These predictions are continuously refreshed as new information is received, enabling operational teams to detect risk patterns earlier in the process. Those predictions values can be used as is by our API users but are also translated into comprehensible signals.
CargoAi said the predictive layer is built to support real operational decisions across the ecosystem. Airlines can spot shipments that haven’t reached RCS or MAN before cutoff, trigger alerts to stations or GSAs, free up blocked capacity, prioritise high‑risk cargo, and benchmark station performance.
Freight forwarders gain early warnings on “At Risk” shipments so they can fix missing documents, coordinate pickups, or update customers using predicted NFD times. Ground handlers and system integrators can apply conservative P90 predictions to prioritise acceptance, automate pre‑alerts, and feed risk levels into dashboards or SLA tools, reducing manual checks.
CargoAi said the predictive engine combines historical performance data by airline, route and product type with live flight schedules and operational events, layering this onto standardized milestone structures aligned with CargoIMP and IATA ONE Record.
Each milestone is paired with predicted timestamps and confidence levels, while an alerts field indicates current risk—low, medium or high—with brief contextual notes to guide operational decisions. The system remains backward‑compatible, and existing Track & Trace API connections do not require changes.
AI Predictive Tracking is available across both CargoAi's platform and API ecosystem, supporting a range of operational and technical environments. The solution can be activated using existing CargoAi connectivity, with alerts automatically refreshed whenever milestones are updated or flights are rescheduled.
Addressing operational volatility in air cargo
CargoAi said "with increasing schedule variability, tighter cutoffs and higher service expectations, predictive intelligence is becoming a critical component of air cargo operations. By shifting from reactive exception handling to earlier risk detection, CargoAi’s Predictive Tracking aims to support more resilient and data‑informed operational workflows."
