vision Ai for brand compliance

Automate Brand Inspection & Optimize Cost

The Client

Two-wheeler OEM

With a network of 4,000 showrooms and service centers catering to a customer base of over 44 million, this automobile behemoth was looking for a custom AI solution to automate brand inspection and track brand anomalies across their sales and service touch points.
image showing brand compliance

Akaike’s edge-cloud agnostic solutions offer great flexibility.

Flexibility 0%
Cost 0%

Executive Summary

Industry Overview


The automobile industry is changing rapidly. Autonomous driving, electric power-trains, digital services, and mobility platforms have become the key pivots to an industry on the verge of disruption. Much like tech businesses who have opted for digitization and increasing automation to become customer-centric, automotive and mobility players are reinventing themselves for the future.
Business Challenge

Customer Experience

In-store experience is critical to purchase decisions and customer loyalty. The client wanted to streamline and ensure consistency across all visual brand touchpoints within the showroom and service centers extending to franchisee operated outlets. They wanted the manual inspection of brand placements and visual merchandising which was human effort intensive, slow, and prone to errors, replaced with a Vision AI alternative.

The Akaike Edge

Inbuilt libraries, DL models with transfer learning capabilities

Experienced ML and DL Ops teams

Efficient Deployment 0%
Integration 0%
Ongoing Maintenance 0%


Inbuilt libraries, DL models with transfer learning capabilities
Step 1.

Capture images of brand placement around the outlet

A high-resolution digital phone camera was used to capture images of points and formats in which the brand was physically displayed.

Step 2.

Identify and label brand discrepancies

Using Vision AI algorithms, batches of images were sorted and relevant images compared to the corresponding visual brand guidelines.

Step 3.

Transfer top level findings to dashboard

The drawn insights were then transferred to an easy-to-use graphical interface that summarized this information into actionable data.

Research shows that 87% of Vision AI
projects do not yield expected results

either owing to training data insufficiencies stalling the project, or being too slow to deploy. Our AI experts can help you accelerate in data sparse environments.

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