Machine-learning start-up Text IQ raises $12.6m in Series A funding

20 Jun 2019

Apoorv Agarwal. Image: Text IQ

Some of the funding Text IQ has drummed up in a new round will be used to bring the company’s Cork office headcount up to between 10 and 15 people.

Text IQ has raised $12.6m in a Series A funding round. The company helps corporations parse unstructured and highly sensitive data and convert it into actionable insights.

This major funding round was led by FirstMark Capital, which boasts firms such as Pinterest, Shopify, Airbnb, Riot Games and more in its portfolio. Sierra Ventures also participated. Text IQ has raised $16m since it was first incorporated in 2014.

Text IQ uses a combination of natural language processing and graphical models for deep learning in order to extract information from text data. It currently works with around 150 businesses and government agencies around the world.

The company has said that as a result of the injection of funding, it plans to hire across a broad swathe of roles including R&D, engineering, product development, finance and sales. A source has told that some of the funds will be used to grow the company’s Cork office from two people up to between 10 and 15 people by the end of the year.

The company originally started as co-founder Apoorv Agarwal’s Columbia thesis project, which saw him create an algorithm that could read a novel and grasp the social hierarchies and interactions between characters therein.

The platform started out as a tool used by corporate legal teams to help them trawl through reams of documents and conversations in order to extract evidence or information.

“The status quo for this is to use search terms and hire hundreds of humans, if not thousands, to look for things that match their search terms,” said Agarwal. “It’s super-expensive, and it can take months to go through millions of documents. And it’s still risky, because they could be missing sensitive information. Compared to the status quo, Text IQ is not only cheaper and faster but, most interestingly, it’s much more accurate.”

Eva Short was a journalist at Silicon Republic