Enterprises today have a confusing array of options for processing documents. Optical Character Recognition (OCR) software from companies like Abbyy was first used to digitize documents nearly 30 years ago. Next, companies like IBM built data capture solutions that used templates to automatically extract data from structured and semi-structured (e.g., invoices) documents. However, these solutions required creating a template for each vendor to account for variability across invoice formats. More recently, cloud vendors like Google, Microsoft, and Amazon have introduced document AI solutions that have two key advantages over previous solutions:
However, these cloud AI solutions were designed with developers in mind, not business users. For instance, uploading documents for processing and downloading the results in a JSON format requires using API calls. Effectively leveraging these solutions can be complex, requiring users to parse the data to extract the desired fields, add post-processing to correct for OCR errors, and create a human-in-the-loop (HILT) interface for quality assurance and processing low-confidence outliers. Building a complete application that satisfies security, privacy, access control, and scalability requirements requires additional development. Additionally, cloud AI solutions from large providers require ongoing maintenance and updates to take advantage of new capabilities and improvements.
To learn more specific details, check out our blog where we compare super.AI to Microsoft Azure for automated invoice processing: Automating Invoice Processing: Super.AI vs. Microsoft Azure
The challenges that come with building and maintaining these document processing solutions has given rise to 75+ companies chasing the Intelligent Document Processing (IDP) market, which is expected to reach $6.38B by 2027. Most IDP vendors have done a great job simplifying setup and adding a human-in-the-loop interface, allowing shared services and global business services departments to automate document-centric processes such as procure-to-pay (P2P) and order to cash (O2C). IDP solutions allow users to:
However, even the best IDP solutions available today struggle to process more than 80% of semi-structured (e.g., invoice) and complex documents (which may include handwritten notes, approval stamps, and/or signatures). They’re also unable to process unstructured documents such as contracts.
Super.AI takes a unique approach to unstructured data and document processing that allows our platform to process 100% of even the most complex documents. There are a few specific reason our technology is able to outperform other IDP offerings: