Customer Success Stories

Customer Success Stories

Enhance automation with unmatched OCR & ICR accuracy

Enhance automation with unmatched OCR & ICR accuracy

Intelligent Document Processing to scale your business

Explore some examples of our customers that have optimized their document processing thanks to IDA – Intelligent Document Analysis – and thus improved workflow automation.

Leveraging unmatched OCR & ICR technology as the cornerstone of our machine learning-based document classification and intelligent data extraction, we provide an extensive range of solutions for automating various document processes. Let our expertise drive your business into the future of efficient and intelligent document handling.

Mail Automation

  • 95 % OCR accuracy
  • Handwriting recognition
  • 99 % automation
Mail Automation

Scaling through market-leading OCR

Golem.ai, a French software vendor, enhanced its automated email processing solution “InboxCare” by integrating PLANET AI’s IDA Recognition.

This integration improved OCR accuracy to over 95% and automated more than 99% of end customer workflows, even for challenging document types like handwritten content. The collaboration showcases the successful fusion of advanced AI technologies to achieve exceptional efficiency and accuracy in high-volume document processing.

Hidden Logo

Scanning Service Provider

  • 91 % automation
  • 80 % time saving
  • Business scaling
Scanning Service Provider

Rule-free Records Classification

Our renowned client has been offering business process outsourcing services to healthcare providers, the public sector and enterprise customers for over 50 years.

They struggled with an automation rate of only 50% for document classification.

Digital Archivist

  • Handwriting transcription
  • Full-text search
  • Increased automation
Digital Archivist

Improved accessibility for customers

AM is a publisher that specializes in creating primary source databases for the humanities and social sciences.

Since primary sources often contain handwritten text of varying quality and damaged scans, the editorial team had to manually index documents with challenging-to-read handwriting and historical styles.

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