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With over a decade of experience in document analysis, our research lab has developed advanced algorithms that excel in the detection, extraction, and analysis of document data. These algorithms are highly effective, simplifying the automation of even the most complex documents. There's virtually no document they can't handle.
Lending decisions made faster and less risky by fully automating and standardizing the underwriting process
Uncomplicate your AR and AP processes by eliminating the manual work that goes into invoice and purchase order matching
Shatter the black box of manual mailroom sorting and bring transparency and control to how insurance documents gets processed
World’s first IDP platform to offer take-it-to-the-bank accuracy with no human review needed for risk-free decision-making
The way most IDP platforms work is that they receive a document as input, do their magic, and extract important business information from these documents. This information extracted at the end can be returned to the business systems in two broad ways - one for consumption by a human being and the second for consumption by another system.
For human consumption, IDP systems display this information in a user interface where the customer can verify the information and correct it if needed. This information can also be returned via a file exchanged over email or shared storage options like Google Drive, Dropbox, etc. That is the second means of returning this information.
For consumption by other systems instead of human beings, this information is returned via an API or Application Programming Interface. The API response can be readily consumed by other technologies like an ERP system or an RPA bot. So when you look at an IDP system, you should think about who is going to consume this information eventually. If that consumer is a system or a Bot, you will need an API response.
This is a great question. IDP solutions roughly use 5 technologies: Computer Vision, Predictive Analytics, Natural Language Processing, Machine Learning, and OCR. Most IDP solutions use multiple OCR engines. Some of them have their own OCR engine, some use third-party OCRs. Most IDP platform vendors will tell you what they use. But as a customer, you should focus on OCR performance rather than worrying about which OCR engine is being used. The reason is that even if an IDP platform uses third party OCR, it might have done significant pre- and post-processing on OCR input and output. So, you will get more accurate extraction compared to using that OCR engine directly.
Try your complex, low quality documents on different IDP platforms and see which one handles your documents better. You should go with the better solution rather than a specific OCR engine.
Absolutely!
In fact, true IDP solutions are full platforms rather than just products. Their strength is in notsolving one problem really well but rather handling all aspects of document data extraction. Youcan design a specific solution and workflow based on this platform that best suits your business.A comprehensive IDP solution needs some level of configuration along with the out-of-the-boxcapabilities.
The answer to this question largely depends on the type of documents. For some documents, one month of data is enough to learn to get things started. For more complex documents, it may take 3 months of data. Infrrd’s approach is to get the customer started with what they have. How many documents you have isn’t the critical issue. The important thing is that every day or every week as you process documents, you see your accuracy numbers climbing.
You can start with zero documents on day one, if you want. Though this will mean you will also start with zero % accuracy. This is not such a bad thing for customers who process all of their data manually. But they will see the manual effort going down every week and see the accuracy climbing.
Cloud and on-premise are the two main means of deploying IDP solutions. There is a third, lesser-known solution, desktop-based IDP applications. These applications are quite light in nature, do not use extensive machine learning, and can be installed independently on the user’s machine. More mature IDP platforms that can process complex documents will need a few servers - either on premise or in the cloud. There are two main criteria to help you choose the best solution for your business:
Data Privacy: Due to the nature of the sensitive data that a business handles, sometimes it is not an option to let that data go out of the business’ control. In that case, businesses prefer an on-premise solution where they keep the data locally. The downside is that the on-premise solutions are costlier, do not always get the latest algorithmic enhancements, and need technical support staff to support it.
Cost: Since the cloud-hosted option is shared, it costs less. You also save costs by not investing in your own infrastructure or the technical support team. The downside is that your data will leave your system and be processed in the cloud.
There are two sides of this coin. Most IDP solutions excel at a particular type of document - structured, semi-structured, or unstructured. But on the other hand, businesses do not have just one type of document. Every business generally has a combination of all three types of documents. That is why most businesses find themselves in a situation where they pick a really good solution for forms, but then realize that it cannot handle semi-structured documents as well. It needs customization to handle these documents.
As we explained in our recent blog post regarding document types, the broader systems are easier to configure. If you find a system that can handle unstructured documents, it is not configured to handle structured documents like forms. But the reverse is not true. So, you should figure out what is the broadest category of documents that your business needs to process. If it is semi-structured documents, then pick an IDP platform that excels at semi-structured documents and configure it for other documents.
Machine learning systems are in some ways like Chinese bamboo trees. Just like with these trees you need to train them to ???, you need to invest time up front training a system on your specific documents. It tries to learn variations, understand layouts, and build a foundational capability to support the prediction wave that is about to come. But once it takes off, it gives you amazing accuracy.
Here is reference data from an actual live implementation of Infrrd’s IDP platform for a document that had thousands of variations. It was near impossible to create and manage templates for this type of document that did not follow a fixed format, and data was never in a fixed position on the document.
This is how the accuracy improved with each iteration of training:
By the time the customer started using the final version of the model, it had a very high accuracy rate.
While other traditional systems provided a 65% accuracy rate against the initial accuracy of 63% of this model, in a few weeks, it outperformed every other accuracy benchmark that the customer had experienced. It feeds off of the corrections made by the data processing teams as well as the new document layouts and variations that it had not processed before. When the system processes a document and even when the results are not corrected, it learns that it has performed the right extraction. This increase in accuracy is made possible by continuous relearning using our feedback loop.
Different types of IDP solutions can process different kinds of data. Infrrd’s IDP platform excels at processing semi and unstructured data. We handle most of the semi-structured data out of the box. Because of the broad nature of unstructured data, your specific implementation may need some customization on top of our platform.
Capabilities to handle visual data like tables, logos, signatures, and symbols are built into our platform. The unique value proposition of our platform is its ability to learn from each and every customer’s data. Our visual recognition engine can predict logos for one customer and predict shipping handling symbols for another.
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