MRO teams faced unstructured manuals and maintenance logs, time-consuming manual search, and difficulty extracting actionable insights. Documentation was scattered, multilingual in some workflows, and not easily queryable for decision support or audit.

Key challenges included:

• Technician time spent searching manuals
• Unstructured maintenance logs and repair narratives
• Slow, manual lookup per task
• Need for compliance-preserving, audit-ready retrieval

Our approach includes LLM-powered transformation of MRO documentation: information extraction from maintenance records, conversion of unstructured logs into structured and machine-readable data, and compliance-preserving semantic search and reranking for task retrieval.

The solution enabled:

• LLM-based extraction and structuring of repair knowledge from unstructured docs
• Structured, searchable knowledge (e.g. knowledge-graph-style) for decision support
• Compliance-preserving retrieval: semantic search and LLM reranking; FAA/EASA aligned
• Faster task lookup and reduced manual search; multilingual MRO workflows supported

Research-backed approaches aim to reduce technician lookup time while preserving regulatory compliance.

1. 1LLMs extract and structure information from maintenance records and logs
2. 2Unstructured narratives are converted into controlled, machine-readable formats
3. 3Semantic search and LLM reranking enable fast, compliance-preserving task retrieval
4. 4Structured knowledge supports decision support, analytics, and audit trail

• Dramatic reduction in task lookup time
• Compliance-preserving MRO task search
• Structured knowledge from unstructured docs; better decision support
• Actionable safety and repair insights; support for multilingual MRO

• Less time searching manuals; more time on maintenance
• Structured, audit-ready MRO documentation and retrieval
• Improved predictive analytics and safety insights from maintenance data
• Scalable across large and small–medium operators