Full Pay as you GO – Why “Full” is key?

Human revision role in a data capture operation

When running a Data Capture operation our competitors are keen to mention the accuracy level, but they actually don’t guarantee (near) 100% accurate data backed by SLA contracts like DocDigitizer does.

Our competitors, because they don’t guarantee (near) 100% accuracy, rely on customer’s human revision operation to curate the data. The problem is two fold:

  1. First the cost of human revision team is at least 55% of the cost
  2. Scale up or scale down an human operation is not easy. Manage overload is even more difficult.

Full Pay as you GO – “Full is the operative word”

DocDigitizer is revolutionizing the Data Capture space.

Full means our API cost includes everything, from automated data extraction to guaranteed (near) 100% accuracy by having a human in the loop operation. At the same time we provide a 70% cost reduction on the Total Cost of Ownership of a Data Capture operation.

What that does mean for you as an enterprise ?

Ultimate Guide to Document Processing: Redefining Intelligent Data Capture

no code data capture

Deploying an operation with DocDigitizer takes 2 hours time. No human teams setup.

Our competitors forget to mention that you have to deploy a human revision operation that may take weeks or months, or that you have to retrain and requalify a human team for the new platform.

no code data capture

With DocDigitizer you can scale up and down.

Again, your competitors forget to mention the cost of a over capacity team deployed. Or what happens to your business when the revision team cannot fullfil the business SLAs response times. Because we have a human in the loop “cloud” that means we can optimize resource allocation and transfer the productivity gains to you.

no code data capture

DocDigitizer human in the loop is more productive than inhouse human in the loop.

We are not only creating awesome AI/ML extraction technology. We are building technology that better manages humans. We are using AI/ML to actually better manage the human-in-the-loop. Because our gross margin depends on how good we are managing both automated extraction and human in the loop, we are building tech to optimize the entire process. Our competitors cannot do that because they cannot guarantee a scalable and uniform process on the human-in-the-loop.