We are exploring how large language models and retrieval-augmented generation can transform Post Operational Flight Track Analysis — making deep safety insights as accessible as a conversation.
Our Objective
Today's operational teams are comfortable using conversational AI tools for research and analysis. We aim to bring that same intuitive, question-and-answer experience to Post Operational Flight Track Analysis.
This is an experimental, additive capability — not a replacement for existing Post Operational Safety Analysis workflows. The goal is to lower the barrier to insight, allowing analysts to surface meaningful patterns without navigating complex data systems manually.
Think of it as giving every analyst a knowledgeable colleague who has read every flight record and is ready to answer questions — instantly, accurately, and without sending anything outside your organisation.
No query language, no complex filters — just natural questions about your operational data.
Every answer is derived from your actual flight records, not general AI knowledge.
Local deployment only. Your operational data never leaves your environment.
Technical Approach
RAG enables local language models to answer questions about your specific operational data without ever retraining the model. The right information is retrieved on demand — like a skilled analyst pulling the right files before answering.
Flight Data
Operational records ingested from your data source
Embedded Index
Semantic model captures meaning, stored on-premise
Retrieval
Relevant records pulled when a question is asked
AI Response
Grounded, data-backed answer delivered to the analyst
Operational flight data is automatically processed and indexed through an embedded AI model that captures semantic meaning — not just raw values. The resulting knowledge base lives on secure, on-premise infrastructure, keeping sensitive data off public clouds.
Analysts interact with their data through a natural-language chat interface — the same intuitive experience as modern AI assistants. Ask questions in plain English and receive grounded, data-backed answers drawn directly from your operational records.
A web-based visualization layer renders flight tracks in motion, allowing analysts to replay proximity conflicts and advisory events frame by frame — turning tabular safety data into an intuitive spatial narrative.
Conversational Interface
Instead of navigating complex dashboards, analysts can ask questions in natural language — such as a safety summary for a specific airspace, timeframe, or set of aircraft. The system retrieves the relevant flight records and generates a structured, plain-English response.
Responses include threat classifications, advisory summaries, and proximity assessments — all derived from your actual operational data, not generalised AI knowledge.
Overall Safety Summary
Based on the flight records for the specified period, the analysis indicates a moderate risk level with multiple proximity events detected. TCAS RA advisories were issued across several tracked aircraft.
Why It Matters
All processing happens on your servers. Flight data never leaves your environment — no public APIs, no cloud dependencies.
RAG retrieves relevant records at query time. The model reads your data on demand — just like a skilled analyst pulling the right files before answering.
Surface safety insights in seconds that would otherwise take hours of manual correlation across large operational datasets.
An additional capability layer, not a replacement. Your existing workflows and Post Operational Safety Analysis platform remain untouched.
The model isn't memorising your flights — it's reading the relevant ones on demand. The result: accurate, data-grounded answers without sending anything to the cloud, and without retraining a model.
These capabilities are under active development. We are committed to delivering AI-powered safety analysis that is private, accurate, and purpose-built for aviation operations — not retrofitted from general-purpose tooling. Reach out if you'd like to learn more or discuss how this could fit your environment.
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