WorkFusion raised $220M at a $340M Valuation led by Georgian

The intelligence automation cloud platform WorkFusion has raised $220M, making the overall raise of the company $340M. The company decided to use the fund for the acquisition of customers and the expansion of the workforce. workfusion series georgian 340mwiggersventurebeat.

Automating conventional monotonous chores previously performed by humans through this intelligent process automation has grabbed the market’s attention. Forrester has also estimated that this field, along with numerous AI subfields in 2019, has led to the foundation of job opportunities for more than 40% of the companies. Most startups employ digital workers rather than humans.

Max Yankelevich and Andrew Volkov founded the company in 2010, which now operates with 300+ employees. The aim was to replace manual knowledge work with AI. It gradually developed the concept further by combining the automation of robotic processes, AI, and operational analytics to increase business operations,  along with name screening, news screening, transaction screening, email processing, and treasure and custody operations.

Although the number of competitors in the market is no less, including Blue Prism, Kryon, and FortressIQ, the company has managed to stand apart from the crowd by speculating on the needs of enterprise users in industries such as insurance and banking and focusing on them. WorkFusion is currently used by some of the top banks of the world, including Standard Bank, Deutsche Bank, and Axis Bank.

Georgian led WorkFusion’s series F announcement. It’s the first fundraising round of the New York City-based company since 2018 when it raised  $50M through series E led by Hawk Equity and Declaration Partners.

The automation solutions are backed by cloud-federated bots, which help automate complicated and hefty document operations, especially in the healthcare, banking, and insurance sectors. The models for machine learning for specific use cases are previously trained with surviving databases, and customers’ databases are used to refine and filter the models.

However, the learning of the bots takes place in real-time from the end users and various data. However, these learnings are accumulated and shared by the bots throughout the ecosystem for a better creation of the effects of an intelligence network from which every customer can benefit well.