AI Agents for Product Data Enrichment
6 min read
Product data is the backbone of e-commerce. But keeping it complete, accurate, and up-to-date is a nightmare — especially when you're managing tens of thousands of SKUs.
The Manual Process
A typical product enrichment workflow looks like this: someone opens a spreadsheet, copies a product name, searches Icecat, checks GS1, maybe Googles the manufacturer's spec sheet, then manually fills in 30 fields. Repeat 500 times.
Enter the PIM Agent
I built an AI agent that does this autonomously. It connects to structured data sources like Icecat and GS1 via their APIs, but here's where it gets interesting: for products that aren't in those databases, it launches a browser agent.
This browser agent navigates the web like a real person. It searches for the product, identifies relevant pages, extracts specifications, and maps them to the right fields in your PIM system.
How It Works
The agent pipeline has three stages:
- Structured sources — Query Icecat, GS1, and other APIs. If we get a match with high confidence, we're done.
- Web browsing — If structured sources fail, the browser agent takes over. It uses a combination of search strategies and page analysis to find product data.
- Validation — Every piece of data gets a confidence score. Low-confidence enrichments are flagged for human review instead of blindly applied.
The Results
What used to take a team of 3 people working full-time now runs in the background. The agent processes hundreds of products per hour with accuracy that matches (and often exceeds) manual work.