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RailMind AI
A railway-operations prototype exploring agent workflows, dispatch review, and an operations interface.
Project archive
Historical project; AI-assisted development. Individual and team contribution has not been independently verified.
The idea
The problem
Railway dispatch suggestions need an operations view where a person can inspect disruptions and deliberately approve or override a recommendation.
The implementation
What I built
The inspected implementation connects dispatch review, explicit approval or override, disruption records, and event staging. This describes the source scope, without claiming operation on a live railway.
- Python
- FastAPI
- React
Features
- Review dispatch recommendations, approve a proposal, or record a manual override.
- Inspect train disruptions and their details through dedicated operations routes.
- Stage events in a transactional outbox and expose an audit workflow.
Architecture
- React operations interface backed by versioned FastAPI routes for trains, disruptions, recommendations, and audit.
- Separate prediction and stream-service modules; a SQLite-backed outbox stages events before relay.
Engineering decisions
- Keep a proposed recommendation separate from an approved or overridden dispatch decision.
Validation and measured results
- Local source review covered recommendation approval/override routes, disruption routes, and TransactionalOutbox. No dispatch, prediction, or load benchmark was rerun.
Limits and unfinished work
- Operational safety, live data freshness, and real railway performance are not established by this prototype.
- Older source can contain superseded descriptions. Numerical impact, adoption, awards, and deployment claims are not carried into this entry.
Evidence and sources
This project’s evidence remains local. Public source and downloadable receipts are not attached yet.