Datenschmiede.ai
datenschmiede.ai provides an AI-powered data quality platform that helps B2B trading companies clean and maintain their master data, including product, customer, and supplier records. The company's configurable matching engine combines established algorithms with modern language models to deliver high-precision results, enabling measurable improvements in just a few weeks. Their use-case-driven approach targets specific data challenges like duplicate detection and article classification, with continuous quality assurance via REST API integration.
- Artificial Intelligence
- Software Only
Funding
Founders
Product
Problem
Most companies manually maintain only 10–20% of their data, leaving the majority of product, customer, and supplier master data incomplete, inconsistent, or duplicated. This poor data quality hampers e-commerce operations, limits personalization and upselling opportunities, and creates significant operational inefficiencies that manual data management cannot resolve.
Solution
datenschmiede.ai provides a configurable AI-based software platform that cleans and continuously maintains entire master data sets across articles, customers, and suppliers. The company's approach is use-case-driven, starting with a workshop to identify specific data challenges and define measurable success criteria. Their KI-based matching engine combines established algorithms with the latest language models to deliver high-quality results with minimal manual effort. After initial data optimization, the engine is integrated via REST API into existing processes, ensuring data quality is maintained long-term without requiring ongoing manual data stewardship.
Target Audience
Primary customers are B2B trading companies and e-commerce businesses that need to improve master data quality to enable better search results, personalization, upselling, and inventory management.
Features
- KI-based matching engine combining established algorithms with modern language models for high-precision data matching and classification
- Use-case-driven implementation covering article classification, duplicate detection, and article family grouping
- REST API integration for continuous, automated data quality maintenance within existing business processes
- Coverage of all master data types: article, customer, and supplier data, including handling of duplicates, inconsistent values, and different representations
- Workshop-based onboarding with system landscape analysis and clear implementation roadmap with measurable success criteria