Traditional document AI demands large, high-quality labeled datasets to start with. Docugym removes that barrier: Docugym lets you get started with document AI even if you don't have any AI-ready dataset to start with.

Docugym's pragmatic approach to document AI grew out of years of hard-won experience at large US financial services companies that handle millions of documents each year:

Document AI can deliver significant enterprise value to a company's operations—but only to the extent you are able to adapt large AI models to your company's specific data.

And that is easier said than done.

The need to adapt AI models to your own data is one of the key obstacles to, and a hidden cost of, AI adoption.

Docugym solves this problem: It lets you turn a few labeled documents into a production-ready document AI system.

It gives your domain experts—your team members most familiar with your company's documents—the tools to evolve your document AI models incrementally and with minimal effort, requiring no in-house AI or MLOps expertise.

The AI Cold-Start Problem

For an AI model to work well on your company's data, the model needs to adapt to the nuances of your company's documents. And to adapt, the model needs high-quality data to learn from. That AI "training data" must closely mirror the production data you use in your business processes.

Without that initial AI-ready training data, a model cannot adapt to your business data, limiting the model's effectiveness. Without sufficient data at the start, your document AI engine will have a hard time getting up and running, and an equally rough time to keep going.

That "cold-start" problem is similar to trying to start your car's engine on a chilly winter morning: The engine will struggle to start up and will need to warm up before the car can run smoothly. In cold climates, drivers use engine block heaters to overcome this problem. In document AI, you need a similar tool to help bootstrap your system. Docugym is that "engine block heater" for your document AI.

How Much Data Do You Need to Start With?

To adapt a traditional document AI model to your company's data, you typically need at least a few hundred example documents in each document category※.

That data must be carefully prepared: Garbage-in, garbage-out applies to AI systems as well. Mistakes in the dataset cascade down to the resulting model, curtailing the model's performance.

That's why the data must be prepared by your domain experts—they are the business users who know what they are interested in when working with those documents.

Document AI is not primarily an IT project: It needs heavy, ongoing involvement from your most skilled and experienced business managers.

Otherwise, your AI model will miss important cues and nuances, which will make the system less useful. Lack of a well-calibrated model is the main culprit if your document AI project fails to deliver real enterprise value.

24x Reduction of Effort

Real-world production systems must handle many document categories. One of our major US financial services company customers, for example, utilizes 132 actively used document types during loan underwriting.

That does not include document subtypes—variations on document categories, such as US state-specific documents, or client-specific documents.

You want all of those documents to work with the AI system. If only a few of those document categories work with the AI system, that creates an even bigger problem: Your team now needs to handle both AI-analyzed and manually analyzed documents—an error-prone, split-personality business workflow.

Suppose you wanted to create a starter dataset with just 100 carefully prepared examples in each category. For this customer, that means 13,200 document examples prepared and vetted by the company's domain experts—the most critical people at the company busy managing and running the business.

If a business expert spends just 2 minutes carefully annotating each document, that is 2 x 13,200 = 26,400 minutes, or about 440 hours of work from your most knowledgeable employees.

And this is not a one-time effort: Document data will not stay the same over time. New document categories will be introduced, and your business folks will likely want to tweak the kinds of data they need from the existing documents, too.

Without continuous monitoring and engagement, your model will degrade over time, diminishing the enterprise value you get from your document AI system.

Pulling the most knowledgeable and experienced business experts into the ongoing task of managing a document AI dataset, is not an acceptable proposition for most companies.

For the customer with the 132 document categories? We needed about 13,200 carefully prepared examples. How many did we actually get? We got 278 document examples over a 2-month period.

That was all their domain experts could assist with before being constantly pulled back into urgent business matters. And that was just not sufficient to make a traditional document AI system to work.

Clearly, there had to be a better way.

What if the system needed just 1 or 2 example documents in each category?

Can we still adapt a document AI model, even with a tiny set of document examples? And can the system grow from those handful of examples, gradually and incrementally, in a self-improving manner?

If you could have at most 2 labeled examples per document category—and, to ensure high quality, require that 2 experts independently prepare those documents, with the system reconciling any disagreements—you could reduce those 440 hours to about 18 hours, or about 24-fold†.

That is the problem Docugym solves.

The Docugym Advantage

Just like you build muscle not in one day, but over a longer period of consistent, sustainable visits to the gym, Docugym allows you to grow your document AI muscles incrementally, with bite-size involvements from your domain experts. It does that by:

• Drastically reducing the need for high-quality document training data, while preserving high model performance.
• Giving your domain experts control over the entire document AI lifecycle without requiring much time commitment.
• Allows for continuous, live evolution of your document AI model.

We'll be thrilled to show you how the Docugym approach delivers value to your enterprise today. Schedule a demo

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※ You may be able to get away with fewer labeled documents using some prompting techniques with vision-language models, but at only at the expense of increased response times from your model and increased costs. For the AI aficionados, Docugym takes full advantage of in-context learning at the appropriate stage of your model, balancing response times and costs.

† 132 document categories x 2 examples each x 2 independent experts labeling x 2 minutes per document per expert = 1,056 minutes, or about 18 hours