The computer revolution in law took flight in the 1970s with the release of the iconic red “UBIQ” terminal. This innovation completely changed how legal document management was performed. It empowered lawyers to easily browse case law online rather than looking through towering racks of yellowed paper. As the years passed, a wave of new document management solutions emerged.

Let’s be honest – AI can seem like a bit of a mystery, and with this mystery comes myths and misconceptions. Is it actually that good? Can it handle varying document structures? Can it integrate with my existing systems? Because of this mystery, many companies have yet to take the leap and incorporate AI into their data processes. Today, we’re going to play MythBusters, separate fact from fiction, and show how you can use AI document processing to maximize efficiency and save costs.

Healthcare data is set to soar, with projections showing that it will grow from 2,300 exabytes in 2020 to an impressive 10,800 exabytes by 2025. To put that in perspective, that’s like having enough data to fill over 2.5 billion DVDs! What’s more is that a large portion of this data is unstructured—scanned documents, handwritten notes, and PDFs that don’t easily integrate into traditional systems. This is where Intelligent Document Processing (IDP) comes in.

Many vendors have started calling their platforms “low-code” or “no-code.” Competitors often pit the terms against each other. But what do low-code and no-code really mean? And what's the difference between the two? Both platforms offer improvements over traditional high-code approaches. The biggest differences are the target user groups.

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

In the world of LLMs, size doesn’t matter. It’s how you generate output that counts. Generative AI (GenAI) adoption rate in organizations jumped from 33% to 65% this year, which means if your organization isn’t leveraging AI, it’s time to get on board or get left behind. One powerful way enterprises are leveraging GenAI is by training and deploying private Large Language Models (LLMs).

Retrieval-augmented generation (RAG) is gaining traction, and for good reason. As businesses and AI experts search for more intelligent ways to process information, RAG combines the best of both worlds, i.e., the vast knowledge of retrieval systems and the creative power of generation models. But what exactly is RAG, and why is everyone talking about it?

What do you think of when you think of generative AI? Generating photos, animations, and videos? Coding and solving math problems? Writing content and brainstorming with a chatbot? These have all driven plenty of excitement around AI, but there’s so much more to it than that! From an enterprise perspective, Generative AI’s impact on intelligent document processing technology is remarkable.

Every device, transaction, and interaction in our digital world generates an endless stream of data. By 2025, the amount of global data is expected to reach a mind-boggling 180 zettabytes. So, how do we extract and make sense of this growing data? That’s exactly where generative AI proves its value. This blog explains generative AI applications for document extraction and how this technology helps cut through the noise and zero in on exactly what you need.

Every business handles numerous document types—contracts, purchase orders, reports, invoices—you name it. And the thing about documents? They never look the same. One day, you’ve got a well-organized PDF with neatly labeled sections, making it easy to find what you need. The next, you’re stuck with a document that’s all over the place—random tables, text scattered everywhere, or even a scanned image that doesn’t fit the mold.