Collins Aerospace, part of RTX, supplies avionics, airport IT and data services, with AI in its Ascentia predictive maintenance and FlightAware Foresight arrival predictions.
With about 80,000 staff, it also makes InteliSence cabin analytics and ARINC SelfPass biometrics; its conventional avionics and airport networks are not counted as AI here. Ascentia predicts component failures and manages repeat defects and aircraft-on-ground events, and Foresight uses machine learning to estimate arrival times.
Qatar Airways chose Ascentia for its Boeing 787 fleet in November 2025. At Tokyo Haneda (HND) Collins supplied the kiosks, bag drops and gates for the Face Express biometric system, running on NEC's face recognition, and it trialled SelfPass at Bristol (BRS).
Collins has owned FlightAware, the Houston flight-tracking company behind Foresight, since 2021.
Aviation AI deployments and updates
01 / Contracted
Collins Aerospace modernisation of Nadi, with a biometric stage planned for 2028
Fiji Airports signed a FJ$17.5 million partnership with Collins Aerospace to modernise Nadi, the country's main gateway, Biometric Update reported in June 2025.
Stage two, in 2028, was to add biometric self bag drop and facial-biometric e-gates.
In 2019 Bristol Airport trialled Collins Aerospace SelfPass, a single biometric enrolment used for check-in, bag drop, security and boarding, alongside Dublin, Biometric Update reported on 6 May 2019.
Turnkey aircraft data management platform combining the Collins Aircraft Interface Device (AID) hardware with a cloud-based portal and software applications for Electronic Flight Bag (EFB), flight data quality assurance (FOQA/MOQA), aircraft condition monitoring, and automated software loading.
The problem it addresses
Replaces manual, physical media-based flight data retrieval processes by automatically transferring aircraft sensor data, flight recorder data, and maintenance information to airline operations and maintenance teams after each flight, enabling faster safety analysis and predictive troubleshooting.
Managed services platform providing nose-to-tail connected aircraft connectivity, consolidating multiple datalinks (legacy SATCOM, VHF, HF, broadband satellite, Wi-Fi, cellular) into a single turnkey solution.
The problem it addresses
Simplifies airline transition to connected aircraft by replacing multiple separate datalink contracts and manual data download processes with a single managed connectivity and data delivery service, reducing cost and improving data availability speed.
Next-generation cloud-based common-use passenger processing system (CUPPS) that allows multiple airlines to share check-in desks, kiosks, and boarding gate positions at an airport. Available in full cloud, hybrid, or on-premise deployment.
The problem it addresses
Reduces airport infrastructure duplication by enabling resource sharing across airlines at check-in, gates, and bag drop. Eliminates the need for each airline to maintain dedicated terminal hardware and reduces on-site server requirements.
Key features
Common-use passenger check-in and boarding gate management
Cloud-based (cMUSE) and on-premise (vMUSE) deployment options
Hybrid deployment across airport networks
Interoperable with all current CUTE and CUPPS platforms
8 more features
Workstation provisioning in minutes via standard PC or thin-client
Integrated with SelfServ self-service kiosks, SelfPass biometric solution, and BagLink baggage management
Data mining and analytics for operational insights and billing
Dashboard reporting for operational decision-making
Sandbox environment for airline application testing
24/7 managed service support
Scalable bandwidth - pay only for resources used
Supports IATA CUPPS standard
Technical specifications (5)
IATA CUPPS standard compliant
Amazon Web Services (AWS) cloud infrastructure
Compatible with traditional PC-based edge devices and peripheral firmware
Supports over 300 airlines at 100+ airports (vMUSE platform)
cMUSE supports 80+ airlines across all four Heathrow terminals
Integrated avionics system for business and commercial aircraft featuring touchscreen flight displays, synthetic vision, performance-based navigation, head-up display, and modular connected architecture. Replaces legacy CRT-based avionics suites with high-resolution touchscreen LCDs and DO-260B compliant ADS-B Out.
The problem it addresses
Addresses pilot workload, airspace modernization compliance (NextGen/PBN mandates), CRT display obsolescence, and situational awareness gaps in business aircraft flight decks.
Shared wide area network (WAN) managed service providing airlines, ground handlers, and maintenance providers with a single IP connection to passenger check-in systems, back-office systems, and core business applications at airports. Operates via the ARINC Ground Network (AGN) enterprise network.
The problem it addresses
Eliminates the cost and complexity of multiple dedicated network connections at airports by providing a single, shared high-reliability WAN connection for all aviation stakeholders at a site.
Key features
Single shared WAN connection for airline check-in and back-office systems
IP access to remote host passenger check-in and boarding applications
Supports back-office connectivity for airlines, ground handlers, and maintenance providers
Fully managed service with 24/7 global support
4 more features
Fully scalable bandwidth when deployed with ARINC MUSE
No new hardware required when deployed with ARINC MUSE cloud
Deployable on other common-use platforms
Secure virtual network space per airline
Technical specifications (4)
Sub-second response times
99.999% network reliability (five nines)
Data managed through ARINC Ground Network (AGN) enterprise environment
Cloud-based predictive maintenance and prognostics platform for commercial airlines, built on Collins' OEM systems expertise.
The problem it addresses
Reduces unscheduled maintenance events, aircraft-on-ground (AOG) time, and flight delays/cancellations by converting disparate aircraft sensor data and maintenance logs into actionable predictive maintenance recommendations before failures occur.
Key features
Repeaters application: NLP-based automatic clustering of maintenance log errors and recurring fault codes
AOG Management with Aircraft Health Monitoring (AHM): combined real-time and historical fleet availability tracking
Prognostic Health Monitoring (PHM): low-code/no-code analytic developer studio for custom predictive alerts
Processes data from multiple aviation sources into a single maintenance dashboard
6 more features
Physics-based modeling, statistical analysis, and machine learning for component health predictions
Near real-time sensor data analysis against historic fleet records
Spare parts provisioning and resource allocation recommendations
FlightSense managed services option with 24/7 expert support
Planned integration with FlightAware flight tracking platform and ARINC Hermes messaging
Supports approximately 1,300 aircraft tails on the platform as of April 2025
Technical specifications (6)
Natural language processing (NLP) for maintenance log parsing (Repeaters)
Big data analytics and machine learning (Azure Data Factory, Databricks Lakehouse, Apache Spark)
Statistical feature extraction (~168 statistical features per flight)
Low-code/no-code PHM analytic developer studio
Cloud-based with web application interface
Covers approximately 40% of Boeing 787s in operation (as of November 2025)
REST-based flight data API providing real-time, historical, and predictive flight information. Grants access to flight position, status, schedule, operator, and route data for any airport, operator, or city pair.
The problem it addresses
Provides aviation operators, technology providers, and flight departments with programmatic access to comprehensive flight data for flight planning, resource management, maintenance scheduling, and competitive analysis without building proprietary data collection infrastructure.
Key features
Real-time flight tracking data via API
Historical flight data back to January 2011
Data filterable by airport, operator, city pair, time range, and other parameters
Advanced filtering for precision queries
3 more features
Intuitively categorized data to simplify queries
Supports flight planning, resource management, and maintenance scheduling use cases
Data sourced from ATC systems in 45+ countries, FlightAware ADS-B network (195 countries), Aireon space-based ADS-B, and datalink providers
Technical specifications (6)
REST API (AeroAPI)
ADS-B terrestrial network with tens of thousands of receivers across 195 countries
Aireon global space-based ADS-B integration
Data from ATC systems in 45+ countries
Datalink integration via ARINC, SITA, Satcom Direct, Garmin, Honeywell GoDirect
Historical data coverage from January 2011
Certifications and approvals
General Services Administration (GSA) contract (U.S. Government)
AI-powered predictive flight analytics toolkit providing machine learning-based predictions for aircraft arrival times, gate arrival times, taxi-out times, and arrival runway assignment. Built on FlightAware's global flight dataset and powered by deep learning neural networks.
The problem it addresses
Enables airlines and airports to anticipate flight arrival and departure times hours in advance with higher accuracy than operational estimates, allowing proactive management of gate assignments, ground crew positioning, connection protection, and irregular operations.
Key features
Predicted wheels-on and gate arrival times available hours before landing
Taxi-out time predictions for departure planning
Arrival runway predictions with shifting probability distributions
Neural network-driven estimated times of arrival (ETAs)
4 more features
Gate assignment predictions using ground movement insights
Global coverage for every operating flight and airport
Real-time updates as flight progresses
Available via API for third-party integration
Technical specifications (4)
Deep learning neural networks for ETA prediction
Machine learning models across multiple prediction types
Built on FlightAware's global historical and real-time flight dataset
Profile data from the Aerostack companies register; deployment records from the verified register. Evidence grades: T1 first-party confirmation, T2 credible reporting. See Our approach for how records are graded.
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Aerostack tracks AI deployments across airports and airlines, with the published evidence behind each record. Photography is real aviation imagery. Record thumbnails may show the named airport or airline; other photographs do not.
