Fleet intelligence for city-scale shared mobility.
A fast-growing urban mobility platform needed more than a rider app. It needed a scalable operating layer that could track connected bikes, predict demand, rebalance availability, manage billing and support, and give riders a simple way to move across congested cities.
Shared mobility does not fail only at the app layer. It fails when the city is not visible.
For a bike-sharing platform, the customer experience is shaped by a simple question: is a usable vehicle available where and when the rider needs it?
Behind that expectation sits a complex operating problem: vehicles move, demand shifts by location and time, assets need maintenance, and field teams need to act before availability gaps become rider frustration.
Demand changed by zone and time
The system had to help teams understand where demand was forming, not just where rides had already happened.
Fleet state needed to be readable
Connected vehicles had to be monitored for location, availability, usage, defect status, and maintenance needs.
Rider access had to stay simple
The app needed to keep discovery, unlock, ride tracking, issue reporting, and trip completion clear.
Operations needed control
Billing, support, CRM, telematics, analytics, and field workflows had to support one operating model.
A connected platform for riders, fleets, billing, support, and operations.
The solution connected the rider journey with the operating surfaces required to manage a distributed mobility network.
Fleet management software
Real-time visibility into bike location, availability, defect tracking, maintenance workflows, and rebalancing decisions.
Demand prediction model
Forecasting support for station-level pickup demand using historical patterns and operating signals.
Data exchange hub
Telematics, sensor signals, IoT-enabled bikes, and operational data flowing between systems.
Billing management system
Usage-based billing, pricing flexibility, account management, and invoicing workflows.
CRM and support visibility
Customer history, service issues, support lifecycle, retention signals, and business visibility.
Rider mobile apps
Bike discovery, QR unlock, ride tracking, issue reporting, personal stats, and trip completion.
From rider demand to fleet control.
Demand signal
Historical ride patterns, station activity, location behavior, and contextual data helped estimate where demand was likely to appear.
Fleet visibility
Connected bikes could be monitored for location, availability, usage, and issue status.
Rider activation
Riders used the mobile app to find a bike, scan a QR code, unlock the vehicle, complete the ride, and end the trip.
Rebalancing intelligence
Demand prediction and location monitoring helped operations teams understand where vehicles needed to be redistributed.
Business control
Billing, CRM, reporting, and analytics created a management layer for usage, revenue, support, operational performance, and customer behavior.
The rider app created access. The operating platform created control.
What changed when the fleet became operationally legible.
Fleet availability was harder to forecast across high-density urban zones.
Demand forecasting helped anticipate rebalancing needs.
Operations teams needed better visibility into bike location, utilization, and maintenance needs.
Real-time location visibility gave teams better control over distributed assets.
Vehicle rebalancing could become reactive rather than predictive.
Demand and location signals helped teams act earlier.
Billing, support, fleet data, and rider experience risked operating as separate workflows.
Fleet data, rider activity, billing, CRM, and analytics moved into a more connected operating model.
The hard part was making a distributed fleet operationally legible.
Every vehicle was a moving asset
Every ride changed the supply map and every unavailable bike created a local experience failure.
Availability had to be predicted
The platform needed to show where demand was forming and where the fleet should be repositioned.
Maintenance affected experience
Defects, downtime, and field workflows had to connect to the rider experience and operating view.
The interface had to stay simple
Riders needed a clear app while the operating platform handled complexity behind the scenes.
Mantra can build connected operating systems for distributed mobility assets.
Real-time fleet visibility
Connected bikes, availability, defect status, and operations signals moved into one control layer.
Demand forecasting support
Demand signals helped teams understand rebalancing needs before availability gaps became rider frustration.
Integrated rider journey
Discovery, unlock, ride tracking, reporting, rewards, and trip completion stayed simple for users.
Business operating layer
Billing, CRM, support visibility, analytics, and telematics exchange supported city-scale operations.
The capabilities behind the build.
Experience
Rider journeys for finding, unlocking, riding, reporting, and completing trips with minimal friction.
Data
Demand, spatial, location, usage, telemetry, and operational signals made usable.
Intelligence
Forecasting support helped the platform move from reactive fleet management to more predictive rebalancing.
Platform
Fleet management, billing, CRM, telematics exchange, analytics, and mobile apps were connected as an operating system.
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