Many companies are already exploring what AI can do for them. We observe a rise of GenAI uses cases that automate the process of extracting information from documents, know as document intelligence. When it comes to document intelligence, companies often turn to methods such as Retrieval Augmented Generation (RAG) to pull insights from documents. Discussions on RAG often overshadow other aspects of the process. Recently, we had the chance to push the boundaries of Document AI to automate a labor-intensive manual process. Here is what we learned.
We partnered with a leader in the insurance industry that processes numerous technical insurance documents on a regular basis. These documents are very diverse, filled with tables, formulas, text, and explanations in a domain specific terminology. Currently, a large team of experts painstakingly “parses” these documents and integrates them into their system. This process requires significant domain knowledge, time, and financial resources. While there’s clear potential for automation, the diversity of these documents has made it extremely challenging – until now.
Over the last three months, we developed a system capable of extracting critical information from these documents. We’ve set all of this up in AWS using Bedrock, Lambda, Event Bridge and a third party OCR service. Here’s what we learned.
