Who it is for
Station operations managers, network controllers, and safety or compliance teams that currently prepare shift-end, weekly, or incident-pattern summaries manually.
A local Generative AI assistant built to explore how prompt-engineered LLM workflows could support operational risk analysis and executive-style reporting — applying 20+ years of airline operations context to a self-hosted AI stack.
Most operational risk reporting in airline environments is manual: someone reads incident logs, performance data, and audit notes, then writes a summary for leadership. This project explored whether a locally-run, open-source LLM could assist with that workflow — surfacing recurring risk patterns and drafting executive-style summaries from operational data, entirely on local hardware with no cloud API calls.
A fully local pipeline with no external inference or cloud API calls. The language model ran locally through Ollama on a single machine, while Open WebUI provided the Docker-hosted interface.
The original session logs no longer exist — the project was decommissioned after running into hardware limits (see note below). The exchange below illustrates the kind of prompt workflow the assistant was designed to support, reconstructed for this showcase rather than copied from a saved transcript.
The technical prototype was designed around a real operational reporting problem: turning fragmented incident information into a concise management view without removing human review or operational accountability.
Station operations managers, network controllers, and safety or compliance teams that currently prepare shift-end, weekly, or incident-pattern summaries manually.
Based on my operational experience, one shift-end or weekly incident summary can require 30–60 minutes of log review and writing, depending on incident volume. This is an experience-based estimate, not a measured project result.
A team-ready version would require a quantized or appropriately hosted model, a proper data pipeline, access controls, output validation, and testing with real operational users before any leadership reporting use.