Rail Intelligence Platform
Riven brings predictive intelligence to rail operations — cutting unplanned downtime, aligning schedules to real demand, and recovering revenue lost to delay.
A single delayed commuter train triggers a cascade: penalty clauses, knock-on disruptions, staff overtime, and passenger compensation — all before 9am. Operators are flying blind on fleet health, demand signals, and energy draw simultaneously.
"Operators who make this transition first are building a reliability and cost advantage that will persist for the entire lifecycle of their fleet."
The rail industry is a $400B+ asset base managed largely on calendar-based inspection cycles from a previous century. Riven ends that.
Riven integrates onboard telemetry, wayside sensors, ticketing data, and timetable feeds into a unified intelligence layer for rail operators.
Continuous monitoring of bogies, traction motors, braking systems, HVAC, and door mechanisms. Riven predicts component failure windows before they surface — eliminating calendar-based inspection cycles that leave money and safety on the table.
Real-time and historical passenger load modelling, fused with weather, events, and ticketing signals. Riven recommends timetable adjustments that reduce overcrowding, cut empty-run energy waste, and improve on-time performance.
Dynamic pricing signals and yield management for franchise and TOC operators. Riven surfaces elasticity curves per route and time window, identifying uncaptured revenue and compensatory fare structures that protect ridership.
Energy draw profiling across the fleet with eco-driving advisory, regenerative braking optimisation, and substation load balancing. Operators reduce energy bills by 15–20% without touching the timetable.
Riven's architecture sits between your existing SCADA, onboard computers, and ticketing stack — no rip-and-replace. It reads your data, contextualises it, and surfaces the decisions that matter to control room staff, depot engineers, and revenue managers alike.
Riven connects to onboard telemetry, wayside IoT sensors, GTFS feeds, POS ticketing, smart card data, and weather APIs through pre-built connectors. No bespoke integration work required for major rail platforms.
Multi-modal ML models trained on fleet-specific failure histories, demand cycles, and energy profiles. Each operator's models improve continuously as Riven observes outcomes — the longer you're on the platform, the sharper it gets.
Prescriptive alerts routed to the right person: depot engineers get maintenance windows with parts lead times; control room staff see demand-driven capacity flags; revenue managers receive yield adjustment prompts before the window closes.
Every operator action — acted on or dismissed — feeds back into the model. Riven tracks prediction accuracy per asset class and surfaces drift early. You always know how well it's working, and so do we.
Rishabh started Riven after watching a rail operator absorb seven-figure penalty charges in a single quarter — all from failures that sensor data had already flagged, but no one was equipped to act on.
His background spans operational research, embedded systems, and transport economics — a combination that makes Riven distinctly practical: not a data science project dressed as a product, but an operations tool built by someone who has stood in a control room.
Riven is built on the conviction that the intelligence gap in rail is not a technology problem. It is a product design problem — and one that is finally solvable.
We're working with a small number of operators and transit authorities on initial deployments. If you're running a commuter, metro, or regional rail network — reach out.
rishabh@riven.ai