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AI-Based Document Extraction API

AI-Based Document Extraction API

Overview

Teman Data developed a smart and flexible AI tool that helps businesses pull organized information from all kinds of documents—whether printed, scanned, or handwritten. Unlike older systems that only work with clean, standard layouts, this solution is built to handle real-world paperwork, where formats often vary and the content can be messy. By adapting to different styles and structures, the AI makes it easier to extract accurate data without needing to clean up or reformat files first, saving time and reducing errors for teams that rely on fast, reliable information.
 

Challenge

Many OCR tools sound promising but often fall short in real-world use. They rely on clean, uniform templates to work properly—something most business documents simply don’t follow. With different formats, designs, and conditions, these tools frequently miss the mark. When that happens, teams are forced to enter data by hand, which takes time, drives up costs, and leads to mistakes. The result is slower operations and less trust in the data being used.
 

Solution

Teman Data’s AI tool solved the usual problems with document processing by using smart technology that understands language and adapts to different formats. It can find important details, pull out key information, and organize the results automatically—no matter how the document looks or how messy it is.  

This led to faster, more accurate results at a lower cost, without the need for manual data entry. Businesses could handle large numbers of documents—like contracts, invoices, or handwritten forms—quickly and confidently, knowing the data was both reliable and ready to use.
 

Output

  • Built an AI pipeline to extract metrics from base64 images
  • Supported handwritten and unstructured documents
  • Delivered clean, validated JSON outputs
     

Impact

  • Reduced document processing cost and error rate
  • Improved data extraction flexibility across document types
  • Enabled real-time automation with minimal input constraints
     

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