OCR Is Not Enough. Try Scale Document AI.

Many industries that impact our day-to-day lives, including logistics, manufacturing, financial services, insurance, and government, still run on paper. These documents contain critical information that powers important workflows like clearing shipments past customs, processing insurance claims, underwriting loans, tracking machinery parts, issuing tax refunds, and parsing clinical lab reports. Unfortunately, document processes still involve people manually keying in information into digital systems. These labor-intensive workflows significantly increase the time it takes to extract information, leading to added costs, poor customer experience, and lack of scalability for high volumes of documents.

To tackle this challenge, companies previously used technologies like Optical Character Recognition (OCR). However, OCR solutions only transcribe text without extracting the most relevant fields. Most “intelligent” extraction solutions rely on customers to manually define templates or hard-coded sets of rules to handle data extraction. The rules dictate which part of the document corresponds to a certain field, which means the system can only process the document layouts that fit the original template. But, if you change vendors, document types, or update the document in any way, then you need to manually define another set of rules. Solutions like these take a lot of upkeep and result in poor quality when dealing with complicated, variable document types.

Imagine being able to feed any type of document into a system that can quickly and accurately extract fields and link entities, even from new layouts or formats — all without any setup or maintenance effort on your end.

Tech-forward companies are recognizing the importance of deploying a robust solution for automating entity extraction and linking because high quality, structured data can:

• Improve existing services by making them faster, cheaper, and more accurate
• Enable new products based on the data unlocked.

The challenge is building a system that can adapt to real-world variations, especially across semi-structured and unstructured documents without sacrificing accuracy. Here is how we do it.

Scale Document AI

Scale Document AI relies on our latest technology, Adaptive AI, to deploy refined machine learning models for customers who demand high quality and low latency when it comes to document processing. We leverage base models trained on millions of data points, and further refine those models for each customer use case. This enables us to extract and link entities from highly variable documents in seconds without putting the burden of setup on the customer.

What’s unique about Adaptive AI is that we deliver a solution tailored to each use case to extract the data our customers need at high quality — regardless of any changes in document layouts. Unlike existing solutions, our machine learning models thrive on challenging and varied documents by parsing the structural layout of pages, contextualizing the meaning of words, and understanding the relationships between different fields. We developed Adaptive AI to actually understand the structure and the form fields’ meaning, rather than simply learning where on a document to find a field (e.g. understanding the vendor name instead of instructing that it is usually on top left of document).