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.
The system had to help teams understand where demand was forming, not just where rides had already happened.
Connected vehicles had to be monitored for location, availability, usage, defect status, and maintenance needs.
The app needed to keep discovery, unlock, ride tracking, issue reporting, and trip completion clear.
Billing, support, CRM, telematics, analytics, and field workflows had to support one operating model.
A connected platform for riders, fleets, billing, support, and operations.
Real-time visibility into bike location, availability, defect tracking, maintenance workflows, and rebalancing decisions.
Forecasting support for station-level pickup demand using historical patterns and operating signals.
Telematics, sensor signals, IoT-enabled bikes, and operational data flowing between systems.
Usage-based billing, pricing flexibility, account management, and invoicing workflows.
Customer history, service issues, support lifecycle, retention signals, and business visibility.
Bike discovery, QR unlock, ride tracking, issue reporting, personal stats, and trip completion.
The solution connected the rider journey with the operating surfaces required to manage a distributed mobility network.
From rider demand to fleet control.
- 01Demand signal
Historical ride patterns, station activity, location behavior, and contextual data helped estimate where demand was likely to appear.
- 02Fleet visibility
Connected bikes could be monitored for location, availability, usage, and issue status.
- 03Rider activation
Riders used the mobile app to find a bike, scan a QR code, unlock the vehicle, complete the ride, and end the trip.
- 04Rebalancing intelligence
Demand prediction and location monitoring helped operations teams understand where vehicles needed to be redistributed.
- 05Business 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 ride changed the supply map and every unavailable bike created a local experience failure.
The platform needed to show where demand was forming and where the fleet should be repositioned.
Defects, downtime, and field workflows had to connect to the rider experience and operating view.
Riders needed a clear app while the operating platform handled complexity behind the scenes.
Mantra can build connected operating systems for distributed mobility assets.
Connected bikes, availability, defect status, and operations signals moved into one control layer.
Demand signals helped teams understand rebalancing needs before availability gaps became rider frustration.
Discovery, unlock, ride tracking, reporting, rewards, and trip completion stayed simple for users.
Billing, CRM, support visibility, analytics, and telematics exchange supported city-scale operations.
The capabilities behind the build.
Rider journeys for finding, unlocking, riding, reporting, and completing trips with minimal friction.
Demand, spatial, location, usage, telemetry, and operational signals made usable.
Forecasting support helped the platform move from reactive fleet management to more predictive rebalancing.
Fleet management, billing, CRM, telematics exchange, analytics, and mobile apps were connected as an operating system.