AI for airports, transit,
and logistics terminals.
On-premise computer vision and agentic AI for passenger flow, ANPR gates, ops assistants, and port and yard monitoring, on your own infrastructure.
Airports, transit, and logistics terminals are too busy to watch by hand and too regulated to send data off-site. We layer three vantage points over the same operation.
Three vantage points over one operation
Each vantage point is powered by a different product, working the same terminals from a different distance.
See every queue, gate, and lane in real time
NIST-tested camera analytics measure passenger flow at airports and transit terminals, read plates at logistics gates, and keep a searchable record of every movement.
- Passenger flow analytics
- ANPR / LPR gates
- Searchable record
Turn operations data into answers
An auditable agent answers across schedule, incident, and operations data and resolves routine passenger queries, with every claim sourced back to its origin.
- Ops assistant
- Passenger helpdesk
- Sourced & auditable
Track port and yard throughput from space
On-orbit AI counts port calls and measures berth occupancy and yard utilization across logistics terminals at national scale, with no camera or sensor on the ground.
- Port-call counting
- Yard utilization
- No ground hardware
What it does across the terminal
Passenger Flow Analytics
Measure queues, dwell, and throughput across airport terminals and transit stations. Operators open lanes and move staff before lines build, cutting security wait times and lifting on-time performance.
Powered by VisionAIreANPR Gates & Fleet Tracking
Read license plates automatically at logistics gates and yards so vehicles clear without stopping, with every entry and exit logged for access and billing.
Powered by VisionAIre AthenaOperations Assistant
Ask across operations, schedule, and incident data and get the reason behind a delay in seconds, every answer traceable to its source.
Powered by Athena AthenaPassenger Helpdesk Automation
Resolve passenger queries and disruption updates automatically across every channel, in multiple languages, freeing staff for cases that need a person.
Powered by Athena SatellitePort & Yard Monitoring
Measure congestion, berth occupancy, and yard dwell across logistics terminals from orbit at national scale, with no camera on the ground.
Powered by SatelliteTransport AI, answered
Can't find what you need? Talk to the transport team.
How does AI reduce airport queue and security wait times?
Camera analytics measure queue length, dwell, and throughput at every checkpoint in real time, so operators open lanes and move staff before lines build rather than after. The same live view flags overcrowding early, letting teams act in the moment instead of reacting to a backlog. Across an airport, that means shorter security waits, higher throughput, and better on-time performance.
Can passenger flow analytics work without facial recognition or storing identifiable images?
Yes. Passenger flow analytics rely on counting, queue, and dwell measurement, which do not require identifying any individual. You can run flow analytics without facial recognition enabled and configure the system so it does not retain identifiable images. Because everything runs on-premise, you keep full control over what is processed and what is stored.
How does ANPR automate gate access and fleet entry at a logistics terminal?
Automatic number plate recognition (ANPR / LPR) reads each vehicle's plate at the gate, so authorized vehicles clear without stopping and the barrier lifts on a match. Every entry and exit is logged automatically, giving the terminal one accurate record for access control, fleet tracking, and billing. The same plate reads work across gates and yards so fleet movement stays visible end to end.
Can airport operations AI run fully on-premise for data sovereignty and regulatory compliance?
Yes. Every part of the stack can run on-premise and air-gapped, so video, passenger data, and operational records stay inside your own infrastructure with full data ownership. That makes it a fit for airports, transit operators, and logistics terminals with strict regulatory and data-residency requirements. Nodeflux engines are NIST-tested and already deployed across 8 countries.
What are the main AI use cases for airports and transit operators?
The common ones are passenger flow and queue analytics, ANPR gate access and fleet tracking, an operations assistant that answers across schedule and incident data, a multilingual passenger helpdesk, and port or yard throughput monitoring for connected logistics. Each maps to one product: VisionAIre for video analytics, Athena for the agentic assistant, and Satellite for wide-area port and yard coverage. Route optimization, predictive maintenance, and demand forecasting are out of scope.
How is satellite AI used to monitor port and container yard throughput?
On-orbit AI analyzes satellite imagery to count port calls and measure berth occupancy and container yard utilization across logistics terminals at national scale. Because coverage comes from orbit, authorities track throughput and dwell at ports with no camera or sensor on the ground. This is a logistics throughput view of ports and yards, not maritime enforcement.
Move more people and cargo. Own your data.
From the terminal floor to the container yard, deployed on your own infrastructure.
On-premise AI for airports, transit, and logistics terminals in Indonesia and Southeast Asia
Nodeflux delivers on-premise, NIST-tested AI for airports, transit operators, and logistics terminals across Indonesia, Southeast Asia, and the wider ASEAN region: airport passenger flow and queue management, ANPR / LPR gate access control, fleet tracking with license plate recognition, real-time congestion analytics, an airport operations AI assistant, a multilingual passenger helpdesk chatbot, and port and container yard monitoring from orbit. Built for operators of facilities in the scale of Soekarno-Hatta and the Angkasa Pura network and ASEAN ports, with full data ownership for data sovereignty and regulatory compliance. Route optimization, predictive maintenance, and demand forecasting are out of scope.