Indico, a provider of enterprise AI for process automation, today announced that it raised $22 million. The company says the new funding will enable it to double its headcount in 2021 with hires in sales, partnerships, and marketing while expanding its channel relationships and integration partnerships to broaden its market footprint.
Process discovery and automation is understandably big business. Forrester estimates that robotic process automation (RPA) and related subfields created jobs for 40% of companies in 2019. According to a McKinsey survey, at least a third of activities could be automated in about 60% of occupations, which might be why Market and Markets anticipates the RPA market alone will be worth $493 billion by 2022.
Indico’s platform, which can be deployed in private cloud or on-premises environments or as a managed service, enables customers to automate the intake and analysis of document and image-based workflows across the insurance, financial services, and health care industries. It ingests PDFs, Word documents, and other unstructured files with built-in support for optical character recognition and more. Post-ingestion, the Indico platform processes these documents by applying AI models that can be chained together into pipelines to perform data classification, extraction, and comparison.
Using Indico, developers can overlay additional models, like sentiment- and keyword-detecting models, and explore new insights and signals. The platform also provides dashboards for testing and tuning models and exposing errors, as well as for gathering input from subject-matter experts.
“Despite all the advancements using AI and machine learning to create value around structured data, enterprises are not seeing the same benefits and ROI with unstructured content — all the text, images, documents, contracts, and customer interactions that make up more than 80% of data in most organizations,” Indico writes on its website. “Traditional keyword-based approaches — including taxonomies, classifiers, expert systems, and pre-trained dictionary based systems — are simply too complex, too inflexible, and too expensive to maintain.”
Indico claims to use a technique called transfer learning to deploy AI to unstructured content more effectively. Its dozens of custom out-of-the-box models, which were trained on a dataset containing over 500 labeled examples, ostensibly learn to analyze industry-specific data from just a few dozen training examples. Moreover, they’re as much as 8 times more compute efficient as compared with traditional machine learning approaches, Indico claims.
“By eliminating much of the trial and error typically involved, Indico makes it much more practical to build and deploy custom models to fit specific business needs, without requiring a lot of data science expertise,” the company writes. “[We] built a huge, generalized dataset trained to understand unstructured content. This allows users to train their own, custom models with up to 1,000 times less data than required with other AI and machine learning solutions.”
Jump Capital and Sandbox Ventures coled Indico’s series B funding round announced today. It brings the company’s the total capital raised to $36 million, coming atter 300% revenue growth in 2020 and supplementing prior funding from 406 Ventures, Osage Venture Partners, Hyperplane Venture Capital and Boston Seed Capital as well as a new investment from Nationwide’s venture capital arm.