How AI Transformed Product Data Management for a Leading German Sanitary Wholesaler
In today's competitive eCommerce landscape, high-quality product data isn't just nice to have—it's essential for success. Yet many German SMBs struggle with inconsistent, messy product information that hurts both customer experience and operational efficiency.
At Kalbytes, we recently partnered with a major German sanitary wholesaler facing exactly this challenge. Let me walk you through how our AI solution transformed their product data management and the concrete business benefits they achieved.
The Challenge: Drowning in Inconsistent Product Data
Our client was battling a common problem in the wholesale space: manufacturer product data that varied wildly in quality, format, and completeness. With over 30,000 products in their catalog, manual standardization was simply impossible.
The inconsistencies created multiple business problems:
- Customers struggled to find products using search and filters
- Product comparison was difficult, leading to abandoned purchases
- Staff spent excessive time cleaning data rather than on value-adding activities
- New products took too long to list properly in their Product Information Management (PIM) system
The Solution: GenAI-Powered Data Transformation
Rather than throwing more manual resources at the problem, we implemented a targeted AI solution focused on three key areas:
- Intelligent Data Extraction: Our GenAI system analyzed long-form product descriptions to pull out critical attributes and specifications that were buried in text.
- Structured Attribute Mapping: We developed an ontology matching system that transformed inconsistent terminology into a standardized product taxonomy.
- Quality Assurance Workflow: To prevent AI hallucinations and ensure accuracy, we built a validation station for continuous quality control.
The entire solution was delivered as a Python-based data pipeline that processed data in bulk and output clean, structured Excel/CSV sheets ready for import into their PIM system.
Real Results: The Business Impact
The transformation wasn't just technical—it delivered tangible business outcomes:
- 70% reduction in time required to onboard new products
from manufacturers - 35% increase in product findability
through improved search and filtering - 28% decrease in product return rates
due to clearer specifications - Staff redeployment
from tedious data entry to strategic customer service roles
Perhaps most importantly, the solution scaled effortlessly with their growing product catalog. What once would have required hiring additional data specialists is now handled automatically.
Lessons for German eCommerce SMBs
If your business is struggling with similar product data challenges, here are key takeaways from this project:
- AI is ready now: You don't need to wait for future technology. Today's GenAI solutions can already transform messy, unstructured data into valuable structured formats.
- Start with a focused PoC: We began with a proof-of-concept on a subset of products to validate the approach before scaling.
- Human-in-the-loop remains valuable: While automation handled 90% of the work, strategic human oversight ensured quality and continuous improvement.
- Integration is key: The solution had to work with existing systems rather than replacing them.
Is Your Product Data Holding You Back?
For German eCommerce SMBs, especially those dealing with large product catalogs from multiple suppliers, AI-powered data transformation isn't just a technological nice-to-have—it's a competitive necessity.
The days of manual data cleaning and standardization are behind us. Today's AI solutions can handle these tasks at scale, freeing your team to focus on what truly matters: serving customers and growing your business.
If you're curious about how similar approaches could work for your specific product data challenges, we'd be happy to discuss your unique situation and explore potential solutions.
