The new system applies artificial intelligence to analyze live operational information and automate the stand and gate allocation process. The platform calculates predictive buffers for each flight, replacing traditional allocation methods that rely on fixed buffer times, scheduled flight data and manual planning.
Christiaan Hen, Chief Executive Officer of Assaia, explained the technology: “Airports historically allocated stands and gates using fixed buffer times, scheduled flight data, and manual processes. StandManager replaces this approach with a solution that continuously analyzes live operational information to automate the stand and gate allocation process and calculate predictive buffers for each flight. For example, if the model identifies that a flight is likely to arrive 10 minutes early or late, the stand plan automatically reallocates gates to optimize within the airports rule set. By adjusting buffers dynamically rather than relying on fixed assumptions, airports can make better use of available capacity.”
The system was officially launched ahead of the Passenger Terminal World 2026 conference in London. StandManager complements Assaia’s ApronAI platform, which uses computer vision technology to monitor aircraft turnaround processes and predict operational milestones such as off-block times.
When used together, the two systems link real-time turnaround performance, towing status and delay predictions directly to stand allocation decisions.
The platform has been developed to address the operational environment faced by many airports, where growing air traffic and infrastructure constraints limit operational flexibility.
According to Assaia, the system continuously processes live operational data to optimize stand and gate allocation as conditions evolve during airport operations.
Hen added: “With global passenger growth expected to double by 2053, traffic numbers are outpacing infrastructure development, and airports are facing sustained capacity pressures. By replacing fixed buffers with dynamic predictive buffers, airports can reduce idle time between aircraft arrivals and increase effective stand capacity by up to 5%, without adding new infrastructure.”
To develop the platform, Assaia partnered with Transformers Group, combining Assaia’s operational AI expertise with the partner’s experience in building open-architecture resource management systems.
Bram Kok, Managing Director of Transformers Group, said: “Stand planning is still too often managed through static rules and disconnected solutions. When delays arise, teams must respond under time pressure, often without complete visibility into the real-time situation and its potential impacts. The combination of AI Agents and airport specific optimization strategies supports data driven decisions when conditions change.”
With the introduction of StandManager, Assaia extends its technology portfolio beyond turnaround monitoring to broader airport resource optimization, linking apron performance data directly with stand and asset allocation processes.
Tags: Bram Kok, Transformers Group, Christiaan Hen, Assaia