Why Manual Data Preparation Is Holding Back Your Growth – And How to Change That
Sound familiar? Your product data arrives from various suppliers in all sorts of formats: Excel sheets with 50,000 rows, PDFs with unstructured descriptions, CSV files with no standardization. Your team spends hours manually cleaning and organizing this data before it can even make it into your shop.
A specialized data provider for e-commerce companies faced exactly this problem. What they made of it clearly demonstrates the potential of AI-powered data automation.
The Starting Point: Data Chaos
This company processes massive volumes of product data daily for their e-commerce clients. The problem? The incoming data was disorganized:
Heterogeneous data sources: Suppliers using completely different formats
Inconsistent categorization: Each manufacturer using their own classification
Time-consuming manual work: Employees spent 70% of their time cleaning data instead of adding value
Error-prone handling: Manual work regularly led to inconsistencies
“We had a team of 5 people working on Excel spreadsheets all day,” one employee reports. “It wasn’t efficient, and it wasn’t motivating.”
The Solution: AI-Powered Data Automation
In collaboration with Kalbytes, the company developed a tailored solution that replaced manual processes with intelligent automation.
Core Features of the Developed Platform:
Intelligent Data Ingestion
Automatic import of Excel/CSV files of any size
AI-based detection of data structures and patterns
Preprocessing to clean inconsistencies
Rule-Based Ontology
Structured product hierarchy
100% reliable conversion logic with no AI hallucinations
Customizable rules for industry-specific needs
Automated Text Generation
AI-generated product descriptions from structured data
Consistent tone and formatting
Scalable content production
The Results: Measurable Efficiency Gains
The outcomes were significant:
Time Savings: 85% reduction in manual effort What previously required 5 full-time employees is now handled by 1 person with AI support. This represents a fivefold increase in productivity.
Data Quality: 95% reduction in errors Thanks to rule-based logic and automated validation, inconsistencies and mistakes were drastically reduced.
Scalability: Capability to process millions of records The platform can now handle data volumes that would be unmanageable manually.
ROI: Payback achieved in under 6 months Savings in personnel costs led to a return on investment in less than half a year.
What This Means for Your E-Commerce Business
The lessons from this project are directly applicable to other e-commerce companies:
For Wholesalers with Large Product Catalogs:
Automated enrichment of supplier data
Consistent categorization across product lines
Faster time-to-market for new products
For Specialized Manufacturers:
Transformation of legacy product data into modern formats
Automated content generation for online channels
Integration into existing PIM and ERP systems
For Growing Online Shops:
Scalable data processing without increasing headcount
Improved data quality for better search results and higher conversion rates
Freed-up resources for strategic tasks
The Path to Implementation
The key to success lies in a pragmatic approach:
Start with a proof of concept: Test the technology on a small dataset
Iterative development: Build the solution step by step based on real-world experience
Close collaboration: Work with experts who understand both the technology and your industry
Conclusion: AI Is No Longer a Future Dream – It’s Today’s Reality
This case demonstrates that AI-powered automation is not a far-off vision, but a practical, available technology with measurable advantages. Businesses that act now gain a decisive competitive edge.
The question is no longer if, but when you take the step toward automation. Because while you are still considering, your competitors are already optimizing their operations.
Want to explore how AI automation can transform your e-commerce business too? Contact us for a no-obligation conversation about your specific challenges.
