See what is really happening across your rail infrastructure.
Ralliyo AI transforms inspection imagery, engineering records and asset history into evidence-backed infrastructure intelligence.
COMPUTER VISION · CONDITION INTELLIGENCE · INSPECTION ANALYTICS
A real photo, a real model, the real answer.
This replay shows one inspection photo going through Ralliyo's AI workflow: what was sent to the vision model in Azure, what the model answered, and the findings it produced for an engineer to review. Nothing is edited after the model answers.
- File
- double-track.png
- Size
- 608×365 px, 66 KB
- Source
- Supplied by the Ralliyo team
- Next
- Sent unchanged to the vision model, with Ralliyo's inspection rules
Railway inspections generate enormous amounts of data. Understanding what changed is still difficult.
Ralliyo compares every inspection with the one before it.
The same sleeper, photographed on 1 Nov 2026 and 1 Nov 2026. Ralliyo matched both frames to SL-NC04-184 and flagged the change for engineering review. An engineer decides the repair.
Understand condition across the entire network.
Every section is scored from its open observations. Select a section to open its assets, evidence and inspection history.
Every asset gets a complete condition history.
Observations, imagery, maintenance and documents attach to the asset, so the next engineer sees the whole story.
SL-NC04-184
High priority- Observation
- Potential sleeper cracking
- First observed
- 4 Sep 2026
- Latest inspection
- 21 Oct 2026
- Trend
- Increasing
Ask your infrastructure.
Copilot answers from inspection, asset, maintenance and document records. Every answer links to the records behind it.
8 observations were recorded on North Corridor in the latest inspection of each section (INS-2026-022, INS-2026-045, INS-2026-044, INS-2026-039). 6 are new, 1 increased in severity and 1 is unchanged. 2 require engineering review.
Section NC-04 had the largest decline in condition score (80 → 76 over 60 days), mainly from sleeper, fastener and vegetation observations.
Real incidents. Real imagery. Measured results.
Walkthroughs built from published rail accident investigations, and the AI benchmarked on publicly labelled track photographs, misses included.
From inspection imagery to maintenance action.
CAPTURE
Collect inspection imagery from walks, drones or vehicle cameras.
UNDERSTAND
AI identifies rails, sleepers, fasteners, ballast and potential observations.
COMPARE
Compare current and historical inspections of the same asset.
DETECT
Identify meaningful condition changes, not just individual detections.
PRIORITIZE
Surface the assets that need engineering attention first.
ACT
Review evidence, create maintenance actions and track resolution.
Nine modules, one evidence model.
Five layers between a photograph and a priority.
The application core is cloud-independent. Storage, AI models, queues and authentication sit behind adapters, so the same product runs on AWS today and elsewhere later.
Built for the teams that keep track in service.
AI that assists engineers and never overrules them.
Ralliyo never declares infrastructure safe, certifies assets, authorises trains, opens or closes lines, controls signalling, or dispatches maintenance without approval.