
AR/VR and 3D view QA for India's largest e-commerce platform
How we validated Flipkart's immersive AR/VR product experience, enabling shoppers to virtually place appliances, beauty products, and home goods in their real-world environments before purchasing.
Client
Flipkart
Team
Dedicated QA engineers
Engagement
Ongoing
Platforms
Android, iOS, Web (Chrome, Firefox)
Technologies and tools
Results by the numbers
10+
Real devices validated
2
AR frameworks covered
100%
Product categories tested
5+
Bug patterns identified
Web
Cross-browser 3D coverage
Low
Light and surface condition testing
Here's a bit about Flipkart
Industry
eCommerce
Headquarters
Bengaluru, India
Engagement
Ongoing
Platforms
Android, iOS, Web
Flipkart, India's largest e-commerce marketplace, introduced an AR/VR and 3D product view feature to transform how customers shop for home appliances, electronics, and beauty products. The feature enables shoppers to virtually place items, fridges, TVs, ACs, and more, into their real-world rooms using their smartphone camera, giving them a true-to-scale preview before purchase.
Powered by ARCore on Android and ARKit on iOS, the feature introduced an entirely new class of quality challenges: surface detection accuracy, 3D model rendering fidelity, device fragmentation across hundreds of Android hardware configurations, and consistent AR behavior under varying lighting conditions. Traditional QA approaches were not equipped to validate immersive spatial experiences at scale.
Before and after QAble
Before QAble
With QAble
No established process for testing AR/VR features
3D model rendering untested across device tiers
No low-light or varied surface condition testing
ARCore and ARKit behaviors unvalidated
Multi-device fragmentation risks unaddressed
Web AR limited to Chrome, no cross-browser coverage
No test cases defined for product placement accuracy
User experience gaps in AR flow undiscovered
No category-specific AR testing across product types
Recurring AR defect patterns going unidentified
Our engagement
Our QA engineers joined the Flipkart team with a clear mandate: validate an immersive AR/VR experience that had never been systematically tested before. The first challenge was understanding how 3D and AR fundamentally work, from surface plane detection and model anchoring to depth estimation and camera feed integration. We built our testing knowledge base from the ground up, studying ARCore and ARKit SDKs to understand exactly what could go wrong and why.
On mobile, the scope was vast. Unlike web testing where Chrome and Firefox represent the primary targets, the Android ecosystem spans hundreds of device models with wildly varying hardware capabilities, from flagship phones with dedicated AR chips to budget devices with minimal GPU resources. We designed a multi-device test matrix covering high-configuration and low-configuration devices across major Indian Android brands, ensuring the AR experience degraded gracefully rather than failing silently.
Environmental testing was another critical pillar. We systematically tested product placement across low-light rooms, brightly lit spaces, textured surfaces, plain floors, and outdoor conditions, discovering rendering and tracking issues that only emerged in specific lighting scenarios. Every product category was validated: beauty products using face AR, and large appliances such as fridges, TVs, and ACs placed at true-to-scale in virtual rooms. Each product required individual verification to confirm model accuracy, scale fidelity, and shadow rendering, ensuring users received a trustworthy purchase preview every time.
Engagement highlights
What QAble delivered
Services
3D and AR functional testing
We designed a comprehensive test suite for all AR/VR interactions within the Flipkart app, covering surface detection accuracy, 3D model placement, scale calibration, rotation and repositioning gestures, and AR session stability. For each product category, we validated that the virtual object matched real-world dimensions within acceptable tolerances and that anchoring remained stable as users moved their devices. We built a defect taxonomy specific to AR behaviors, distinguishing rendering glitches, tracking losses, model distortions, and SDK-level failures.
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