Aviation AI for Safety Data Analysis

Themes, which is where the value is

Recurring hazards clustered, and recurring contributory and causal factors clustered separately across human, technical, organisational and environmental categories. Each theme is named, given its frequency and share of the batch, and traced back to the cases behind it — including the cross-cutting threads, like procedural adherence or fatigue, that run across several categories and are invisible one report at a time.

• Hazard clustering across the batch
• Contributory and causal factor clustering, kept separate from hazards
• Every theme traced to the case references behind it

Trends, kept in proportion

Counts and rates by category, flight phase, severity and source type; direction of travel over time; and a Pareto showing which few categories drive most events. This is the insight over the numbers rather than a charting engine — heavy dashboards and FDM analytics belong in a safety management platform, and the page will not pretend otherwise.

• Safety data analysis worked as an eleven-column review grid
• Safety data analysis report, exported as a document

Honest about its denominator

Trend analysis is easy to over-claim, so the mode is built to under-claim. It states the denominator and the limits every time — that a read is based on the cases provided and not normalised per sector or flight hour — and says plainly when a small sample means an apparent trend may not be significant. A missing field is treated as genuinely absent: it will not impute a value to make a cluster look tidier. And it carries a standing reminder that this is decision-support, not an authoritative safety assessment.