TextIQ, a machine learning platform for parsing sensitive corporate data, raises $12.6M

TextIQ, a machine learning system that parses and understands sensitive corporate data, has raised $12.6 million in Series A funding led by FirstMark Capital, with participation from Sierra Ventures.

TextIQ started as cofounder Apoorv Agarwal’s Columbia thesis project titled “Social Network Extraction From Text.” The algorithm he built was able to read a novel, like Jane Austen’s Emma, for example, and understand the social hierarchy and interactions between characters.

This people-centric approach to parsing unstructured data eventually became the kernel of TextIQ, which helps corporations find what they’re looking for in a sea of unstructured, and highly sensitive, data.

The platform started out as a tool used by corporate legal teams. Lawyers often have to manually look through troves of documents and conversations (text messages, emails, Slack, etc.) to find specific evidence or information. Even using search, these teams spend loads of time and resources looking through the search results, which usually aren’t as accurate as they should be.

“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, TextIQ is not only cheaper and faster but, most interestingly, it’s much more accurate.”

Following success with legal teams, TextIQ expanded into HR/compliance, giving companies the ability to retrieve sensitive information about internal compliance issues without a manual search. Because TextIQ understands who a person is relative to the rest of the organization, and learns that organization’s ‘language’, it can more thoroughly extract what’s relevant to the inquiry from all that unstructured data in Slack, email, etc.

More recently, in the wake of GDPR, TextIQ has expanded its product suite to work in the privacy realm. When a company is asked by a customer to get access to all their data, or to be forgotten, the process can take an enormous amount of resources. Even then, bits of data might fall through the cracks.

For example, if a customer emailed Customer Service years ago, that might not come up in the company’s manual search efforts to find all of that customer’s data. But since TextIQ understands this unstructured data with a person-centric approach, that email wouldn’t slip by its system, according to Agarwal.

Given the sensitivity of the data, TextIQ functions behind a corporation’s firewall, meaning that TextIQ simply provides the software to parse the data rather than taking on any liability for the data itself. In other words, the technology comes to the data, and not the other way around.

TextIQ operates on a tiered subscription model, and offers the product for a fraction of the value they provide in savings when clients switch over from a manual search. The company declined to share any further details on pricing.

Former Apple and Oracle General Counsel Dan Cooperman, former Verizon General Counsel Randal Milch, former Baxter International Global General Counsel Marla Persky, and former Nationwide Insurance Chief Legal and Governance Officer Patricia Hatler are on the advisory board for TextIQ.

The company has plans to go on a hiring spree following the new funding, looking to fill positions in R&D, engineering, product development, finance, and sales. Cofounder and COO Omar Haroun added that the company achieved profitability in its first quarter entering the market and has been profitable for eight consecutive quarters.

https://ift.tt/eA8V8J TextIQ, a machine learning platform for parsing sensitive corporate data, raises $12.6M https://tcrn.ch/2Ksymv6