RealityEngines.AI becomes Abacus.AI and raises $13M Series A Posted on : Jul 14 - 2020

RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability.

In addition, the company also today disclosed that it has raised a $13 million Series A round led by Index Ventures’  Mike Volpi,  who will also join the company’s board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company’s board. New investors include Mariam Naficy, Erica Shultz, Neha Narkhede, Xuezhao Lan and Jeannette Furstenberg.

This new round brings the company’s total funding to $18.25 million.

At its core, RealityEngines.AI’s  Abacus.AI’s mission is to help businesses implement modern deep learning systems into their customer experience and business processes without having to do the heavy lifting of learning how to train models themselves. Instead, Abacus takes care of the data pipelines and model training for them.

The company worked with 1,200 beta testers and in recent months, the team mostly focused on not just helping businesses build their models but also put them into production. Current Abacus.AI customers include 1-800-Flowers, Flex, DailyLook and Prodege.

“My guess would be that out of the hundred projects which are started in ML, one percent succeeds because of so many moving parts,” Reddy told me. “You have to build the model, then you have to test it in production — and then you have to build data pipelines and have to put in training pipelines. So over the last few weeks even, we’ve added a whole bunch of features to enable putting these things to go into production more smoothly — and we continue to add to it.”

In recent months, the team also added new unsupervised learning tools to its lineup of pre-built solutions to help users build systems for anomaly detection around transaction fraud and account takeovers, for example.

The company also today released new tools for debiasing data sets that can be used on already trained algorithms. Automatically building training sets — even with relatively small data sets — is one of the areas on which the Abacus team has long focused, and it is now using some of these same techniques to tackle this problem. In its experiments, the company’s facial recognition algorithm was able to greatly improve its ability to detect whether a Black celebrity was smiling or not, for example, even though the training data set featured 22 times more white people. View More