Product Data Governance with AI
Combining FileMaker and artificial intelligence to transform a complex catalog of 20,000+ product references into a structured, reliable, and future-ready business asset.
- — CLIENT NAME : Confidential
- — LOCATION : Europe
- — INDUSTRY : Electrical & Building Technologies Manufacturing
- — TECHNOLOGY : FileMaker + Artificial Intelligence
- — Impact : Improved product data quality, standardized classifications, reduced catalog complexity, and established a scalable foundation for ERP modernization, automation, and future AI initiatives.
Summary
A building automation manufacturer was managing a catalog of more than 20,000 product references that had become increasingly difficult to use due to inconsistent and incomplete data. By combining FileMaker with artificial intelligence, a new data foundation was established to improve information quality and support future ERP modernization initiatives.
The Story
Over time, the growth of this building automation and smart home technology manufacturer led to increasing complexity within its product catalog. Product descriptions varied significantly, attributes were often incomplete, internal classifications lacked consistency, and numerous product variants made searching and identification more difficult.
These challenges created operational issues across the organization. Teams responsible for purchasing, production, logistics, and technical support did not always have access to a consistent view of product information. Rather than simply cleaning the database, the company wanted to establish a reliable foundation that could support the future evolution of its ERP system and manufacturing processes.
The chosen solution combined FileMaker with an artificial intelligence-driven approach. Product information from multiple sources was first consolidated into a structured representation that enabled a comprehensive analysis of the entire catalog.
This process made it possible to automatically identify missing information, detect highly similar products, highlight potential duplicates, and rebuild a more logical classification structure. The resulting insights were then used to define a descriptive coding framework based on actual product characteristics and the company’s business rules.
Today, the product catalog serves as far more than a historical archive. It has become a structured and dependable operational asset that simplifies product identification, search, and management. The new foundation supports better collaboration across departments while preparing the organization for future initiatives involving automation, ERP development, and broader adoption of artificial intelligence.
Project Highlights
Improved Data Quality
Product information was standardized to create greater consistency across the catalog.
Clearer Classification Structure
A logical and uniform framework makes products easier to identify and reduces ambiguity.
Better Cross-Department Collaboration
Teams now share a common language for describing and managing products.
Future-Ready Foundation
The new data structure supports future ERP, automation, and AI initiatives.
Q&A
What business challenge was the company facing?
The organization needed to regain control of a large catalog affected by inconsistent data and unclear classifications.
Why was data quality so important?
How was artificial intelligence used?
AI was used to analyze existing information, identify inconsistencies, and rebuild a more structured catalog framework.
What is descriptive product coding?
It is a coding system that allows users to quickly understand the key characteristics of a product.
What benefits does the company see today?
More reliable data, easier product management, improved collaboration, and stronger readiness for future digital initiatives.