In today’s data-driven economy, the ability to unlock structured information from unstructured documents is no longer a luxury—it’s an efficiency imperative. From contracts and policies to statements of work and regulatory filings, vital business information is buried in PDFs, Word files, and scanned documents. The question facing many organizations isn’t whether to apply AI to generate this information, but how.
Companies often confront an execution fork in the road:
At first glance, building internally with the very visible LLMs may seem easy and cost-effective. But a deeper look reveals why more companies are turning to smaller proven platforms to fast-track results, reduce risk, save scarce resources, and scale intelligently.
It’s tempting to assume that an off-the-shelf LLM can simply be repurposed to extract data from your internal documents to automate a business process. After all, these models can read and summarize content, right?
Here’s the catch:
General LLMs aren’t trained to understand your domain-specific documents and your company’s own nuanced terminology or preferences. They may not extract precise structured and unstructured elements, and maintain auditability and consistency across thousands of pages. They likely don't automatically connect the data to your unique downstream databases or systems as the documents flow in. – a repetitive business process.
They are by nature driven by ad-hoc prompting, due to their consumer and search-centric origin. What they do is a far cry from a production-grade documents-to-data system, with human validation built-in.
Not all AI efforts are created equal. Smart companies recognize that internal teams should focus on AI initiatives that directly drive competitive advantage in their industry—not on building infrastructure or re-solving solved problems.
For example:
Building and maintaining an internal document data extraction system is a costly detour from these strategic priorities. The infrastructure, tuning, and compliance effort required is massive—and unnecessary when robust, proven platforms already put open-sourced and proprietary LLMs to use for this purpose and continuously invests to stay abreast of the ongoing advancements.
Docugami is purpose-built to transform real-world complex business documents into structured, actionable data with speed and precision—so you don’t have to build and support the infrastructure yourself.
Docugami is optimized for domain-specific documents like YOUR legal agreements, contracts, policies, quotes, licenses, RFPs, NDAs, MSAs, SOWs, ACORD forms (like Loss Runs and SOVs), and most other document types, even totally unique ones to your business (and acronyms!). It captures:
Yes, most documents written and negotiated or edited by humans for business purposes are highly nuanced and variable.
Docugami delivers business results in weeks, not years:
Process thousands of documents with ease, including:
Especially in regulated industries like insurance, real estate, finance, life sciences and healthcare administration, Docugami provides secure, SOC2-compliant deployment.
Compared to building internally:
Organizations using Docugami report:
In the AI era, the smartest companies are learning to invest and benefit in two fundamental ways:
They invest, but don’t reinvent tools that already exist. They do invent in areas where internal AI development can create proprietary advantages.
Let Docugami handle document data extraction—so your team can benefit from efficiencies in the AI industry’s continued innovation, while focusing your unique, scarce resources in proprietary work.
Unlock your documents. Unleash your data. Accelerate your business.
Choose Docugami—where AI meets the real world of business documents.