Transforming aviation safety search with semantic AI

In aviation, searching incident and safety reports is anything but straightforward. Reports are written by pilots, engineers, and cabin crew — each using their own abbreviations, colloquial terms, or technical jargon. Existing keyword search missed critical connections: a search for 'bird strike' wouldn't find reports describing 'avian ingestion' or 'FOD event'. Safety analysts spent hours manually cross-referencing reports.

We implemented a multi-layer semantic search pipeline including embedding-based retrieval and a cross-encoder reranking stage. The system understands intent — not just keywords — and surfaces relevant reports regardless of how they were written. Filters for aircraft type, date range, and severity let analysts drill into trends or investigate specific anomalies.

Safety analysts now retrieve relevant reports 30% faster and with significantly higher recall. The system catches connections that keyword search missed entirely, enabling proactive trend analysis that previously required days of manual work.