Platform
1. Corridor and use-case definition
Agree the route, rolling stock context, inspection goal, privacy requirements, and success criteria before data capture begins.
Pilots
De-risk railway AI with a corridor pilot and clear evidence goals.
RailEyes is best evaluated on a defined railway context: a corridor, a train family, an inspection problem, and an operator review workflow. That keeps the pilot practical for railway teams and credible for investment diligence.
Pilot inputs
- Corridor, station area, or route section.
- Target conditions such as vegetation, assets, zoning, or visual corridor review.
- Rolling stock and installation constraints.
- Data protection and anonymization expectations.
- Operator validation method and success criteria.
Pilot model
A disciplined path from feasibility to repeatable value.
2. Retrofit and data capture
Install RailEyes hardware under operator oversight, verify field of view, power, antenna placement, GPS, and operational fit.
3. Processing and review workflow
Run selected media through anonymization, mapmatching, AI-assisted detection, and operator review workflows.
4. Validation and expansion decision
Review findings, false positives, data volume, operator usefulness, and the business case for expanding the pilot or product module.
Operator diligence
What a pilot should prove.
- Can the capture setup fit the rolling stock without harming normal operation?
- Does the data workflow handle connectivity, privacy, and review constraints?
- Are the findings useful enough for maintenance or inspection planning?
- Can operator feedback improve the models and reduce avoidable review burden?
Investor diligence
What a pilot should reveal.
- How quickly a rail operator can move from pilot setup to useful evidence.
- Which product module creates the strongest wedge for recurring adoption.
- How defensible the data, mapmatching, validation, and privacy workflows can become.
- Where the platform can expand after the first operational use case is proven.