Aviation

EASA’s roadmap identifies three AI assurance levels — Level 1a (human-in-the-loop), Level 1b (human-on-the-loop), and Level 2 (human-out-of-the-loop) — each with distinct requirements for validation, explainability, and operational monitoring. DOT’s AI Ethics Framework for aviation maps every AI system deployment to the appropriate EASA assurance level and produces the technical documentation, operational procedures, and human oversight protocols required to support regulatory acceptance of each application.

Yes. DOT’s data architecture engagements include integration with all major aviation MRO platforms — Swiss-AS AMOS, TRAX, Ramco Aviation, and IFS Maintenix. Integration is achieved through the platform’s native API layer, with custom connectors developed where standard APIs do not cover the required data scope. All integration work is validated against the client’s airworthiness data management requirements and data governance standards.

DOT’s GreenOps Intelligence programme for CORSIA automates fuel consumption data collection from flight management and fuel management systems, applies approved ICAO emissions factors, calculates route-level and aggregate emissions relative to CORSIA baseline requirements, identifies offset obligations, and produces the monitoring, reporting, and verification (MRV) documentation required for CORSIA submission. We additionally cover EU ETS aviation and CSRD scope 1 and 3 emissions reporting simultaneously within the same data architecture.

DOT’s OT security approach for aviation maintains strict network separation between corporate IT and operational technology environments — including ACARS, ATC interface systems, ground handling networks, and airline operations control systems. Anomaly detection is deployed to monitor OT network traffic for deviations from established communication patterns. All security architecture recommendations are reviewed against applicable AVSEC and airworthiness requirements to ensure that protective measures do not interfere with flight-critical systems.

For airlines with accessible component history data, a targeted predictive maintenance proof of concept for a defined fleet subset can be delivered within six weeks under DOT’s Rapid AI Pilot programme. Full fleet coverage, including MRO data architecture implementation, typically requires four to six months depending on fleet size and the condition of existing data. DOT’s Data Liquidity Audit produces a precise timeline estimate at the outset of each engagement.