Prevent breakdowns before they stop your rental machinery

• Predictive maintenance

  Schedule the service before a breakdown stops the machine.

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• Anomaly detection

  Abnormal usage patterns flagged automatically, so you act before the stoppage.

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• Remote DTC diagnostics

  Read ECU fault codes remotely, with no physical connection to the machine.

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• Post-rental inspection

  Faster return inspection, so the machine is ready for the next rental sooner.

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Fewer breakdowns, higher fleet availability, better rental profitability

• Fewer breakdowns at customer sites

  We detect the abnormal condition during the rental, before the stoppage.

• Faster turnaround

  Return inspection and workshop work prioritised by real condition.

• Fewer technician trips

  Remote diagnosis avoids on-site visits and reduces costs.

• More billable hours

  Real usage recorded: fewer machine hours left uninvoiced.

• Predictive maintenance and breakdown prevention

  Plan workshop work before a failure interrupts the rental

  View solution: Predictive maintenance and breakdown prevention

• Remote diagnostics

  Know what happened on site before dispatching a technician

  View solution: Remote diagnostics

• Rental fleet availability and profitability

  Know what can be rented, what is working and which usage to bill

  View solution: Rental fleet availability and profitability

• Security, theft and remote control

  Control the machine while it is away from the branch

  View solution: Security, theft and remote control

• Operational digitalisation

  Documented deliveries and returns, without scattered photos or paperwork

  View solution: Operational digitalisation

• Integration and advanced analytics

  One data set for operations, workshop, ERP and billing

  View solution: Integration and advanced analytics

• Automatic AI machine inspection

  Machine condition documented on return, without a different judgement at every branch

  When the rental ends — and before delivery where it applies — the machine is photographed from a mobile phone and computer-vision models identify visible damage and apparent condition issues. The output is not a loose photo: it is a structured inspection result, comparable between check-out and check-in and tied to the rental.

  • Visible damage and condition anomalies detected from images
  • Structured, comparable inspection result for delivery and return
  • Objective evidence when there is a dispute with the customer
  • Linked to the digital delivery note and signature on mobile
  Operational impactFewer disputes and less time between return and the next rental

• Automatic ECU trouble code capture

  The machine's own fault code, visible in the platform without connecting to it

  On machines that support it, B'Smart reads the fault and diagnostic codes generated by the control unit over CAN BUS. Where the machine exposes no standard data, we decode the bus or capture the signal with direct sensors. The code reaches the platform with machine, timestamp and operating context.

  • Manufacturer DTCs received automatically over CAN BUS
  • Bus decoding and reverse engineering when required
  • Event context: hours, temperature, load and location
  • Notification routed by hierarchical alarm
  Operational impactBetter-prepared technicians and fewer inspection visits

• Mobile operator guidance

  Whoever stands next to the machine understands what happened and what to do

  When a trouble code or anomaly is detected, the operator or technician opens the alert on their phone and gets a plain-language explanation of the detected problem and the recommended action. No physical connection and no diagnostic equipment on site.

  • Mobile alert explaining the code or anomaly detected
  • Recommended action and urgency level for the operator
  • No physical connection or diagnostic tool required
  • Escalation to technical service when the incident needs it
  Operational impactFaster response at the customer and less downtime

• Predictive maintenance

  Plan the intervention before the machine stops

  Using each machine's history and current data we anticipate degradation and maintenance needs, and turn them into planned services instead of urgent repairs. Planning follows real usage, not a theoretical calendar.

  • Models over each machine's historical and current data
  • Services planned by real usage
  • Prevention of critical conditions such as DPF clogging
  • Heat maps of pending maintenance and inspections
  Operational impactMaintenance fitted between rentals and more availability

• Automatic anomaly detection

  Deviations detected before a threshold or an obvious failure

  Analytics learn each machine's normal behaviour in its usual work and continuously compare incoming data against that pattern. Abnormal consumption, temperature, load or usage surfaces before any fixed threshold would flag it.

  • Continuous analysis of each machine's real behaviour
  • Abnormal patterns flagged without waiting for a fixed threshold
  • Genset overloads, traction batteries, temperature or consumption
  • Hierarchical alarms by severity and recipient
  Operational impactFewer unexpected breakdowns and greater fleet profitability

• Full fleet tracking, 20% more productivity and device installation in 10 minutes.

  Machinery rental company

  800 machines · 5 branches

• We can now bill for 30% of the machines operating at weekends.

  CEO, machinery rental company

  300 machines

• €3,000 saved per month by avoiding DPF obstruction.

  Machinery rental company

  600 machines