Case Study

A real-time dashboard for tracking mobile inspections, built around geo-fencing and fleet performance.

Client Overview:

Vava Cars runs a fast-moving platform for buying and selling vehicles, and a big part of their service relies on mobile inspectors meeting customers on time, in the right place, with minimal delay.

But without a system for verifying job locations and driver movement, the operations team had to rely on inconsistent updates and manual check-ins. They needed a better way to track performance on the road, and reduce friction between drivers and dispatch.

The Challenge

Vava Cars relied on a mobile inspection fleet to evaluate vehicles directly at customer locations. While ground teams had the right tools, the operations team lacked visibility into how jobs were being completed.

Drivers could manually mark a visit as complete, even if they had not reached the correct destination, which made reporting unreliable and performance difficult to track.

There was no system to confirm whether a driver:

Reached the assigned location.
Followed the intended route.
Checked in on time or completed the job correctly.

This gap created friction between dispatch, ground teams, and customer expectations. Reports were difficult to validate, and delays or missed visits couldn’t be easily resolved.

The client had IoT tracking hardware installed across the fleet, but without an interface to surface or verify that data in real time, it wasn’t actionable.

What They Needed

The team needed a way to connect location data with real-world activity and make it visible in real-time. This meant building a system that could:

Display fleet activity in a way that allows operations to monitor and trust it.
Provide real-time verification of driver locations, arrival times, and check-ins.
Automatically flag jobs completed outside of approved customer areas.
As more mobile units were added, the scale increased without introducing manual overhead.

They weren’t looking for a heavy system; they just needed one that worked in the background, provided the right people with access to clean data, and supported fast decision-making daily.

What We Built

We worked closely with the Vava Cars team to design a lightweight system that connects ground activity with real-time insights. All are designed to integrate seamlessly with existing IoT hardware and operations.

Real-Time Geo-Fence Interface

Created a daily geo-fence builder that lets dispatch define customer zones. Drivers could only log a visit if they were physically inside the boundary, ensuring location accuracy without relying on manual updates.

Fleet Tracking Dashboard

Built a responsive web dashboard that shows active units in real time. Teams can see which drivers are en route, which visits are complete, and whether anyone is off-route or idle longer than expected.

IoT Integration Layer

Connected directly with Vava Cars’ existing tracking devices to pull clean, reliable data into the dashboard, including location, timestamps, and task progress, without extra manual reporting.

Results:

Once the platform went live, the operations team finally had real visibility into what was happening on the ground, without chasing updates or second-guessing reports.

Missed visits and inaccurate check-ins dropped immediately.
Dispatch could track every unit in real time, instead of relying on post-shift summaries.
Drivers stopped marking jobs complete before arriving, since the system now flagged out-of-zone activity.
Idle time was easier to spot, which made route planning and field coverage more responsive.
Ground teams and HQ finally operated on the same set of data, which smoothed out comms and reduced daily friction.

Why It Worked

The system didn’t try to change how the team worked. It supported what was already in motion. Instead of layering in complexity, it made key tasks easier:

Location data now backs every job, so reports are trusted.
Ops no longer need to chase drivers for updates.
Drivers stay focused on the route, not on checking boxes.
Dispatch works off real-time data, not delayed summaries.

Everything built into the system had a reason: reduce second-guessing, shorten feedback loops, and help the team move faster with less overhead. That’s what made it stick.

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