Live Webinar

Document extraction at the cutting edge with LLMs vs. LLMWhisperer

LLMs have become operational powerhouses, thanks in part to their ability to extract rich, meaningful information from documents. But even the best models, in real-world use cases, often depend heavily on the quality of the input they receive.

Discover how LLMWhisperer, Unstract’s dedicated text extraction service, prepares documents for peak LLM performance and sets standards for LLM-ready outputs.

In our upcoming webinar, we put top LLMs to the test—evaluating their performance across documents of varying complexity. We’ll dive into why directly parsing raw documents often leads to subpar results, and showcase the impact LLMWhisperer has on improving extraction outcomes.

What will this session cover?

  • Factors that make accurate document data extraction a challenge today
  • How LLMs are pushing the boundaries of document understanding and extraction
  • A deep dive into LLMWhisperer’s unique approach to making LLM-ready document formats and the business benefits that come with it
  • A side-by-side comparison of top LLMs and LLMWhisperer across real-world extraction tasks
  • Emerging trends shaping the future of document data extraction

      If you’re looking to stay at the cutting edge to unlock business value hidden in the depths of your documents, this webinar is for you. Register now!

      The webinar recording is 
now available
      Speakers
      Picture of

      Mahashree

      Product Marketing Specialist, Unstract

      We help fit unstructured documents into your business workflows

      Unstract is document agnostic. Works with any document without prior training or templates.
      Have a specific document or use case in mind? Talk to us, and let's take a look together.

      Prompt engineering Interface for Document Extraction

      Make LLM-extracted data accurate and reliable

      Use MCP to integrate Unstract with your existing stack

      Control and trust, backed by human verification

      Make LLM-extracted data accurate and reliable

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