Case Study on AICUE DATA LAB - Enabling decision making in manufacturing industry with AI

Case Study: How a Dealership Improved Service Turnaround and Turnover by 10% Using AI

Tue, 14 Oct 2025 00:00:00 +0000

When a leading multi-brand dealership started missing delivery commitments, it wasn’t because of poor technicians — it was visibility. Vehicles were ready but still waiting. Bays stayed full, and same-day deliveries kept slipping.

That changed when the team implemented Platemate AI and AIDeal together. What followed was a 10% increase in service capacity — without adding staff or a single extra bay.

The Challenge: Bottlenecks Hidden in Plain Sight

The dealership aimed for a 24-hour turnaround for all service jobs.
But once the vehicle entered the workshop, visibility vanished.
Cars were often completed but undelivered — waiting for billing, approval, or a cashier.
No one knew exactly how long each vehicle had been on site.

From Cameras to Insights: How AI is Transforming Dealership Operations

Fri, 10 Oct 2025 00:00:00 +0000

Most dealerships already have dozens of CCTV cameras quietly recording every moment — vehicles arriving, bays filling, customers waiting.
What if those same cameras could tell you why a service bay is free, which vehicles are pending delivery, or how long a car has been waiting after completion?

That’s exactly what we set out to do with Platemate AI and AIDeal — turning ordinary cameras into a real-time source of operational intelligence.

How a Tier 1 Supplier Cut Inspection Time by 95% Using VISPECT

Wed, 09 Jul 2025 00:00:00 +0000

A Tier 1 Supplier’s Journey from 15-Minute Inspections to 45 Seconds

In the world of commercial vehicle manufacturing, quality assurance is non-negotiable. For one Tier 1 supplier specializing in fabrication and metal forming, ensuring precision across thousands of parts per month was a serious operational challenge.

Before VISPECT, their most complex part—requiring five different views and 150 individual part identification checks—took around 15 minutes to inspect manually. This created delays, fatigue, and inconsistencies across operators and shifts. Also, this was with an experienced operator, who could only inspect a limited number of parts per hour.