OpenProd.io is an AI-native product data onboarding platform that turns messy supplier files into clean, PIM-ready data in minutes - not weeks.
Built by LemonMind, company with 15 years of experience in PIM implementations across Europe, OpenProd.io solves the most expensive bottleneck in product data management: getting supplier data into yourPIM system.
The problem we solve: Product data teams spend an enormous ammount of time on manual product data entry, mapping, and cleanup. Supplier catalogs arrive as chaotic PDFs, Excel files, and XML feeds - each in a different format, language, and structure. Traditional onboarding takes 4-6 weeks per supplier. OpenProd.io reduces that to hours.
How it works:
- Upload supplier files (PDF, Excel, XML, CSV) or connect via API
- AI automatically extracts, maps, normalizes, and enriches product attributes
- Every extracted value gets a confidence score - you review only what needs attention
- Push validated data directly to your PIM (Pimcore, Akeneo, Ergonode, or custom via REST API)
Key capabilities:
- AI Extraction Engine - Handles any supplier format. Maps fields, cleans values, normalizes units, generates missing descriptions automatically.
- Confidence Scores - Every attribute gets a reliability score. Bulk-approve high-confidence data, focus human review on edge cases.
- Pre-run Cost Estimates - See token usage and cost before any AI extraction runs. No budget surprises.
- Re-Prompt Engine - Fix 100 errors in 10 seconds. Describe the correction in plain language, AI applies it across all affected products.
- MCP Server (Model Context Protocol) - Industry-first: let AI agents talk to your product catalog natively. Query, update, and manage product data through conversational AI.
PIM-agnostic by design: OpenProd.io connects to your existing PIM - we don't replace it. Works with every any system with a REST API.
OpenProd.io is built for Product Data Managers, PIM Managers, Heads of E-commerce, and Operations teams who are tired of Excel hell and need a scalable, AI-powered approach to product data quality.
