In e-commerce, product experience is often the product. If images are inconsistent, categories are messy, and recommendations miss the mark, customer attention disappears quickly. For platforms trying to keep shoppers engaged across hundreds of retailers, the challenge is not just scale — it is creating a visual and personalized experience that still feels seamless.
Karma Advisory helped an e-commerce and social platform do exactly that.
The client operated across more than 300 retailers and needed a smarter way to manage inconsistent product data, improve visual presentation, and deliver more relevant recommendations. Karma designed a machine learning–driven solution that strengthened how products were identified, displayed, and surfaced to users — helping the platform create a cleaner, more engaging, and more personalized shopping experience at scale.
The Challenge
The client’s platform brought together product content from a large and varied retail ecosystem. While that breadth created value for users, it also introduced complexity.
Product data arrived in inconsistent formats. Images varied widely in quality and presentation. Categories were not always cleanly structured. And without stronger personalization, users were less likely to discover products that aligned with their interests. Across hundreds of retailers, those issues made it harder to create a cohesive experience and limited the platform’s ability to fully engage users.
The client needed a more intelligent way to improve product visibility, standardize presentation, and make discovery feel more tailored — without relying on overly manual processes that would be difficult to sustain at scale.
The Approach
Karma designed and implemented a custom machine learning solution that addressed product experience across three critical dimensions: identification, presentation, and personalization.
To improve product organization and accessibility, we developed a deep neural network capable of identifying and categorizing products from images with greater precision. This created a stronger foundation for structuring product data and making items easier for users to find.
To improve visual consistency, Karma implemented a U-Net–based model that automatically refined product images by removing backgrounds. This helped create a cleaner, more polished presentation across the platform and increased the visual quality of the shopping experience.
To strengthen personalization, we applied collaborative filtering techniques that enabled the platform to deliver more relevant product recommendations based on user behavior and emerging patterns. Together, these capabilities helped the client move from fragmented product presentation to a more intuitive, user-centric experience.
What Karma Delivered
Karma delivered a custom ML solution that improved how products were classified, displayed, and recommended across a large retail network.
This included:
- A deep neural network for product identification and image-based categorization
- A U-Net–based image refinement model for automated background removal
- Collaborative filtering logic for more relevant product recommendations
- A scalable ML framework designed to improve user experience across 300+ retailers
The Outcome
The impact was both immediate and strategic.
The platform was able to improve product visibility and overall user engagement by creating a more visually consistent and personalized product experience. Clearer visuals and better recommendations strengthened user satisfaction, helping support higher retention and deeper platform loyalty.
Beyond customer-facing improvements, the engagement also created long-term value for the client. Karma helped develop proprietary machine learning models that strengthened the client’s intellectual property base and contributed to a more differentiated product capability. The solution also reduced reliance on more traditional, labor-intensive methods, generating cost efficiencies while proving the value of tailored ML over off-the-shelf alternatives.
What began as a data and presentation challenge became a stronger competitive asset.
Why It Mattered
In crowded digital commerce environments, customers respond to platforms that feel easy, relevant, and visually polished.
By helping the client improve product organization, standardize image quality, and personalize discovery, Karma enabled a more engaging shopping experience at scale. The engagement shows how custom machine learning can do more than optimize a workflow — it can strengthen customer experience, create proprietary value, and improve competitiveness in markets where attention is hard to win.
Closing Perspective
Karma Advisory helps organizations turn fragmented digital experiences into smarter, more scalable capabilities that drive growth. In this case, that meant transforming inconsistent product data and presentation into a more refined, personalized, and commercially valuable experience — one that helped the client improve engagement today while building a stronger innovation foundation for tomorrow.
