Former Indian Space Research Organisation (ISRO) scientist Ashish Ranjan is bringing aerospace-grade precision to the automotive sector. Having previously worked on high-thrust Vikas liquid rocket engines at ISRO and nuclear reactor systems at the Department of Atomic Energy (DAE), Ranjan has launched Raste, a Silicon Valley-based startup leveraging AI and telemetry data to transform vehicle maintenance into a proactive science.
Key Career & Platform Highlights
- Founder: Ashish Ranjan (Ex-ISRO Scientist, Ex-DAE Engineer, Ex-Lucid Motors Vehicle Architecture Engineer).
- Startup Platform: Raste (AI-powered predictive vehicle maintenance platform).
- Core Technology: Real-time analysis of vehicle sensor data and operating telemetry to catch mechanical failures before they happen.
- Target Impact: Claims to reduce maintenance and unexpected breakdown costs by up to 40% for commercial fleets, rideshare drivers, and daily commuters.
Translating Space and Nuclear Systems Engineering to Automotive Intelligence
During his tenure at ISRO, Ranjan was responsible for the assembly, integration, and testing of Vikas engines for key satellite launches, including PSLV-C46. Later, as a vehicle architecture engineer at electric vehicle OEM Lucid Motors in California, he worked directly on next-generation EV platforms like the Lucid Gravity, integrating complex software, electrical, and thermal systems.
Through these roles, Ranjan recognized that modern automobiles—much like space launch vehicles and nuclear heat exchangers—continuously generate rich sensor telemetry that often goes unutilized until a catastrophic failure occurs.
How Raste Works: Continuous Fleet Monitoring
Traditional vehicle maintenance relies either on scheduled service intervals or reactive repairs following an unexpected dashboard indicator light. Raste replaces this model by functioning as an active, continuous mechanic operating in the background.
Maintenance Approach
Traditional Service Model
Raste AI Predictive Model
Trigger Mechanism
Scheduled mileage or physical breakdown.
Continuous sensor data evaluation in real-time.
Data Utilization
Minimal (read only when fault code triggers).
High (processes full stream of telemetry signals).
Downtime Risk
High risk of unexpected roadside failure.
Minimizes downtime via early failure warnings.
Target Audience
General retail vehicle owners.
Currently deployed across initial fleet customers in the United States, Europe, and India, Raste represents a growing trend of aerospace cross-disciplinary innovation reshaping commercial mobility. By converting complex diagnostic data into clear, actionable health metrics, the platform enables vehicle operators to optimize maintenance schedules and prevent costly unexpected repairs.
