Klassif AI: IDP Software Vendor
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Belgian AI platform specializing in order confirmation processing with human-in-the-loop training and 93% accuracy claims.
Overview
Founded in 2017 by Tom Vermeulen in Leuven, Belgium, Klassif AI operates as a product of Raccoons, targeting order confirmation processing rather than general document processing. That narrow vertical focus is the core strategic bet: where broad IDP platforms compete across document types, Klassif AI optimizes for a single high-frequency workflow - incoming sales orders via email, PDF, or scan.
The platform positions against traditional OCR with 93% accuracy versus 60% for legacy solutions, claiming 87.3% average reduction in manual effort and 2.5x median ROI over six-month payback periods. Customer Stijn Hoegaerts at Van Marcke reports "ten hours saved per day on average." In early 2026, Klassif AI open-sourced its PDF annotation component after three development iterations - a move that builds developer community engagement while signaling technical transparency.
Klassif AI was also selected as one of 10 Belgian tech scale-ups for BEyond Belgium, a year-long PulseFoundation acceleration program offering mentorship and access to industry leaders. CEO Antoine Smets framed the selection as recognition of the company's trajectory: "Being part of BEyond recognizes where we've come and accelerates where we're going." The announcement timestamp suggests a late-2024 date; the program signals early-to-mid growth stage rather than Series A maturity.
How Klassif AI Processes Documents
Klassif AI ingests orders through Microsoft Outlook 365, Google Workspace, Dropbox, and direct ERP connections, then applies machine learning and NLP to extract customer data, product codes, quantities, and pricing. Where confidence falls below threshold, the platform routes documents to human reviewers - corrections feed directly back into the model, enabling continuous improvement through normal employee workflows rather than dedicated retraining cycles.
The open-sourced react-pdf-ner-annotator handles PDF annotation using Tesseract.js for OCR, and was released after three internal development iterations. Model versioning supports production rollback with activity monitoring, allowing teams to revert to a prior model version if a new iteration degrades on edge cases. Typical deployment runs 4-5 weeks with no infrastructure changes required.
Use Cases
Order Confirmation Processing
Manufacturing, logistics, chemicals, food industry, construction, and banking organizations deploy Klassif AI for order confirmation workflows. The platform processes incoming orders via email, PDF, or scanned documents, extracting customer data, product codes, quantities, and pricing with up to 93% productivity increases compared to manual handling.
Invoice Management
Organizations use Klassif AI to process supplier invoices and customer billing documents, with 80% faster processing compared to traditional OCR solutions. Extracted data is validated before passing to ERP systems, reducing downstream correction cycles.
Technical Specifications
| Feature | Specification |
|---|---|
| Core Technology | Machine learning, NLP with human-in-the-loop training |
| Document Types | PDF, email, scanned documents |
| Accuracy Rate | 93% vs 60% traditional OCR |
| Manual Effort Reduction | 87.3% average |
| ROI | 2.5x median, 6-month payback |
| Implementation Timeline | 4-5 weeks typical deployment |
| Infrastructure Changes | None required |
| Open Source Component | react-pdf-ner-annotator (React, Tesseract.js) |
| Integrations | Microsoft Outlook 365, Google Workspace, Dropbox, ERP systems |
| Model Versioning | Production rollback with activity monitoring |
| Parent Company | Raccoons |
Resources
Company Information
Headquarters: Leuven, Belgium
Founded: 2017
Founder: Tom Vermeulen
CEO: Antoine Smets
Parent: Raccoons
For vendors competing in adjacent document types, see DOConvert for supply chain purchase order automation and Paperbox for insurance mailroom processing. For a broader view of human-in-the-loop document processing approaches, the capability guide covers confidence thresholds, review interfaces, and accuracy optimization patterns.