Raillabs provides digital technology for railway safety, delivering accurate, reliable, and affordable data collection and analytics to protect rail infrastructure. Its ARISTA Track Inspection System uses deep‑tech sensors to reduce human error in track monitoring, while the ChakrVue Wheel Shelling Prediction System leverages IoT data to predict wheel failures and enable proactive maintenance. Together, these solutions help rail operators improve safety and operational efficiency.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional railway track inspections rely on manual, labor‑intensive processes that are prone to human error and often miss critical defects. Similarly, wheel shelling on rolling stock is difficult to detect early, leading to premature wheel replacement, safety risks, and increased maintenance costs.
Solution
Raillabs addresses these challenges with two digital platforms. The ARISTA Track Inspection System (TIS) is an autonomous, AI‑driven solution that combines ultrasonic flaw detection, computer‑vision analysis, multi‑sensor fusion, precise GPS positioning and redundant system design to deliver high‑accuracy track defect data with minimal human intervention. The ChakrVue Wheel Shelling Prediction System (WSPS) leverages industrial‑IoT sensors to collect high‑speed wheel data, transfers it reliably to a central analytics platform, and applies algorithmic sliding‑flag detection and predictive models (including the Shelling Analytical Tool) to forecast shelling events and optimize wheel life. Both systems provide real‑time alerts, customizable dashboards, and data‑driven maintenance recommendations, enabling railway operators to improve safety, reduce downtime, and lower lifecycle costs.
Target Audience
Primary customers are railway infrastructure owners, track maintenance contractors, and rolling‑stock operators seeking automated inspection and predictive wheel‑health solutions.
Features
- Autonomous operation of ARISTA with AI‑powered vision and ultrasonic sensors for defect detection
- Multi‑sensor integration and accurate GPS coordination for precise track mapping
- Redundant hardware architecture ensuring reliable inspection under varied conditions
- High‑speed IoT data collection from wheels with reliable communication and transfer
- Real‑time system‑failure alerts and daily sliding index (DSI) for proactive monitoring
- Centralized Shelling Analytical Tool (SAT) delivering predictive analytics and maintenance insights
- Customizable dashboards and reports for both track and wheel health monitoring
- Predictive maintenance algorithms that extend wheel lifespan and reduce human error