Catch the failure before it happensright at the edge.
AutoGuard reads engine temperature, vibration, and pressure signals as they're generated, scores failure risk locally on Raspberry Pi and ESP32 hardware, and tells your fleet team exactly which component to check - days before it would have failed on its own.
One pipeline, from raw sensor to maintenance order.
Every reading moves through the same seven stages, in order - there's no step a vehicle's data can skip on its way from raw signal to a maintenance recommendation on the dashboard.
Reactive maintenance guesses. Predictive maintenance knows.
Most fleets still run on a fixed service calendar or wait for a dashboard light. Neither actually tracks the part's real condition - this does.
- Parts replaced on a fixed schedule, worn or not
- Failures surface as a breakdown, not a warning
- Diagnosis starts only after the vehicle is already in the shop
- No visibility between scheduled inspections
- Parts replaced when sensor data shows real wear
- Failure risk scored continuously, days ahead of impact
- Every alert names the component and the fix
- Edge inference keeps monitoring even offline
Know how many days a part has left - not just that it will fail.
Each tracked component carries a live countdown, so maintenance gets scheduled with lead time instead of guesswork.
The same fleet, seen through four different lenses.
Each console loads scoped to what that role needs to act on - nobody wades through screens meant for someone else's job.
Vehicle Owner
Monitor your vehicle's health, get plain-language alerts, and track full maintenance history in one place.
Owner consoleFleet Manager
Oversee every vehicle at once with fleet-wide analytics, downtime reporting, and maintenance planning.
Manager consoleTechnician
Work an assigned inspection queue, update repair status, and view live diagnostics per vehicle.
Technician consoleService Manager
Coordinate service centers, assign technicians, and track operations across every location.
Service consoleOne console, scoped to what each role needs to act on.
Pick a terminal to enter the dashboard with the right permissions already applied.
Built to move maintenance from reactive to planned.
Economic
Lower downtime losses and reduced unplanned repair spend across the fleet.
Industrial
More reliable equipment and maintenance decisions grounded in live data.
Sustainability
Parts get replaced when they're actually worn - not early, not late.
Operational
Edge inference keeps warnings flowing even when connectivity drops.
The distributions behind every alert on the dashboard.
These are the same aggregate views a Fleet Manager sees - where risk concentrates, how it spreads across components, and what it's done to downtime since rollout.
Fleet teams don't adopt a dashboard - they adopt fewer surprises.
These accounts describe the kind of operational shift AutoGuard is designed to deliver for commercial and passenger fleets.
"We used to find out about a bearing failure when the driver called from the roadside. Now the alert lands two weeks earlier, with a part number attached."
"Edge inference matters more than we expected. Our depots sit in low-signal zones, and the risk scores keep updating even when the network drops out."
"Technicians stopped guessing which part to pull first. The queue is already ranked by remaining useful life, so triage takes minutes, not a full inspection."
Built to fit the Tata Motors vehicle ecosystem.
AutoGuard's sensor mapping and RUL models are tuned for the platforms Indian commercial and passenger fleets already run - from last-mile CVs to SUVs and city buses. Swipe through each class for its indicative specifications.
A live diagnostic scan of every vehicle in your fleet.
This is the same scan your edge nodes run continuously - sweeping engine, brake, and battery zones, flagging anything trending toward failure before it shows up as a breakdown.
Runs on the vehicle, not the cloud
Inference happens on Raspberry Pi and ESP32 hardware onboard, so warnings keep flowing even with no signal.
Trained on real degradation cycles
Models are built on the NASA CMAPSS turbofan dataset's run-to-failure trajectories, not synthetic labels.
Every alert ships with an action
No raw scores without context - each flag arrives with the specific inspection or part to check.
Small habits that add days back to a part's remaining life.
AutoGuard flags the failure - these are the everyday habits that slow the wear down in the first place. Swipe through, or let it play.
From workshop bay to open road.
AutoGuard's sensor package is designed to sit quietly alongside the people and vehicles that already keep a fleet moving.
Empowering intelligent mobility through teamwork and technology.
Four disciplines, one console.
Team Lead
Owns the end-to-end ML strategy - from feature design on the CMAPSS dataset to keeping the model lightweight enough to run on-device. Sets technical direction across the team.
IoT
Wires up the physical layer - vibration, temperature, and pressure sensors feeding into Raspberry Pi and ESP32 boards, tuned for reliable readings under real driving conditions.
AI/ML
Builds and validates the failure-probability and remaining-useful-life models, translating raw telemetry into the confidence scores the console surfaces to fleet teams.
Full Stack
Builds the console itself - the role-based dashboards, the API layer connecting edge nodes to the fleet view, and the responsive front end you're using right now.