AI-Driven Innovation in Aircraft Predictive Maintenance

The Client

Our client is a massive DACH airline carrier and MRO. When combined with its subsidiaries, it is among the top European airlines in terms of fleet size and passengers carried. The airline group has over 700 aircraft, making it one of the largest airline fleets in the world. The group is a global aviation enterprise with more than 530 subsidiaries and equity investments.

The Challenge 

The airline’s predictive maintenance team faced the complexity of processing massive volumes of technical data arriving in different formats and timelines. These included post-flight telemetry and real-time operational data transmitted mid-flight to the Aircraft Condition Management System. There were also manually written work orders, often unstructured, multilingual, and error-prone. en work orders, often unstructured, multilingual, and error-prone.

The fragmented nature of this data environment made it difficult to construct a comprehensive view of aircraft health. Any inconsistency in data quality and structure would hinder insight generation, resulting in reactive maintenance cycles, prolonged turnaround times, and missed opportunities for early intervention.

Additionally, the existing systems required more flexibility to support the diverse needs of internal engineering teams and partner airlines, each with its own unique requirements, expertise levels, and workflows. The airline had to rely on a solution that would be fast, accurate, scalable, and easy to maintain across a wide range of use cases.

The Solution

Scalefocus collaborated with the client’s data science team to build a modular AI-driven framework that could reliably ingest, process, and analyze data from multiple heterogeneous systems.

The backend components, developed by Scalefocus using Python and deployed via Kubernetes on Google Cloud, handled data aggregation, transformation, and pipeline orchestration. Statistical models were also employed to detect anomalies across both real-time and historical datasets, boosting early fault detection and improving response times. 

Another core innovation was the integration of Large Language Models (LLMs) to process and categorize unstructured, multilingual maintenance logs. This transformed noisy field data into structured, actionable insights.

The systems Scalefocus developed are highly modular, reusable, and capable of providing seamless access across multiple teams and airline partners, regardless of their domain expertise. Our experts were also responsible for customizing them so that each stakeholder could adapt the tools to their specific operational needs. This flexibility was vital not just for rapid problem-solving but also to reduce long-term maintenance complexity.

While the client oversaw AI model training, Scalefocus ensured that the surrounding systems provided clean, categorized input and intelligently handled the output, feeding insights directly into operational dashboards and planning tools.

The Results 

• 40% faster anomaly detection and insight generation from flight, sensor, and maintenance data
• Reduced AOG and unscheduled maintenance, enabling more proactive planning and minimizing costly disruptions
• Streamlined workflows through modular design and low-maintenance components
• Standardized, multilingual data processing, including handwritten and unstructured inputs
• Flexible, scalable architecture for use across multiple airlines and technical teams
• Operational transparency through unified view of aircraft condition and maintenance history, providing engineers with actionable insights.