In the fast-growing digital fashion space, wardrobe management platforms rely on accurate and scalable data ingestion to deliver value to users. For one U.S.-based startup, integrating shopping receipts from a wide range of retailers proved to be a significant technical and operational hurdle. With each retailer using different receipt formats, building and maintaining one-off parsers was costly, time-intensive, and unsustainable. Karma Advisory partnered with the client to create a flexible, AI-driven solution that accelerated deployment while maintaining scalability and privacy.
The Business Challenge
A U.S.-based startup providing a digital wardrobe management solution aimed to input products by parsing users’ shopping receipts. However, the varying formats of receipts from different retailers made creating individual parsers time-consuming and rendered the system fragile due to frequent format changes.
The Solution & Implementation
-
Leveraged large language models (LLMs) to develop a generic receipt parsing engine
-
Implemented image-based auto-categorization of products
-
Developed functionality to detect primary colors of products from images
-
Deployed on Azure OpenAI to ensure data security and privacy
The Business Impact
-
Decreased development costs and effort by 80%
-
Reduced time-to-market by 90%, accelerating product deployment speed and efficiency
-
Scaled to support hundreds of retailers within just a few days
Conclusion
By rethinking the data ingestion process with AI-driven automation, the client overcame the limitations of traditional parser-based methods. The solution dramatically reduced costs and time-to-market while enabling rapid scalability across hundreds of retailers. This case demonstrates how applying LLMs to real-world product data challenges can unlock speed, efficiency, and resilience—positioning startups to compete and grow in highly dynamic markets.
