There is No Substitute for Real IDP

Machine learning can make all the difference when it comes to improving business outcomes and reducing costs. These days, most serious Intelligent Document Processing (IDP) vendors use machine learning somewhere in their products to generate maximum accuracy and efficiency. But that’s not enough. More than just having machine learning capabilities, the magic comes from how -- and when -- you put them to work. 

Unlike legacy IDP players, which have machine learning as part of their feedback phase, Infrrd uses machine learning automatically in the document understanding phase. In practice, this means the models our customers create are not built by our programmers, but rather, by a machine learning platform that generates the model. All the user has to do is upload documents and tag fields, and the model builds itself. That’s the real deal. After the building phase, the models continue to learn from your feedback, without the need to intervene.

Legacy Vendors Are at a Disadvantage

Legacy players are at a disadvantage. They gained dominance at a time before true machine learning was developed, which means they are constantly scrambling for bolt-ons that can be implemented in the corrections phase without disturbing the existing flow. This gives them some advanced capabilities, but not a true machine learning-fueled process.

Infrrd decided five years ago to make the switch to a machine learning native approach. That means our insights are driven by the technologies shaping the future. It is important to us because this generates the most speed, accuracy, and opportunity for our customers. 

Building models with machine learning already achieve better results today than its manual counterpart, and we believe this gap will widen further in the years ahead. To illustrate the unparalleled benefits of a machine learning native solution to build models, we offer the following examples: 

New Documents

You want a model for a new and unknown document type (a lease document, insurance policy document, healthcare record, etc.), that the IDP vendor has not seen before. If you are working with a legacy player, you will likely have to wait two weeks or more for the IDP vendor’s developers to build a new template. On the Infrrd platform, you can create a new document type yourself by uploading sample documents in the new format and tagging the relevant fields. Regardless of how the document looks, the model will build itself. You don’t even have to involve us, although we are always here to assist you.

New Language

You want to process documents in a new language (e.g. German). If you work with a legacy vendor, programmers will have to manually tell the system that “Invoice number” is called “Rechnungsnummer” in German. Infrrd’s platform is language-agnostic and can automatically process every single language that is written from left to right. After tagging a few invoice numbers, our machine learning platform will automatically conclude that the word “Rechungsnummer” flags an invoice number nearby. 

Variation

You have a document with 100 slightly different variations. If you are working with a legacy player, there is no learning in the model building phase. You’ll have to figure out -- though it’s yet to be done -- how to build and maintain 100 variations. Infrrd’s platform allows you to build one machine learning model that learns from variations on its own.

These examples illustrate that the machine learning-based building approach allows for significantly more variation and complexity in documents. It also puts you in the driver’s seat - you don’t have to call the IDP vendor with every new document or variation or wait for a new model to be built. You save time and money on models while staying in control. You can also redeploy your manual labor and let machine learning take care of your documents' variation and complexity, worry-free. 

Our machine learning IDP platform gives you the speed and opportunity to generate meaningful insights and accelerate business outcomes by putting machine learning front and center in our technology from the start.