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

TechCrunch | 6/19/2019 | Staff
Kaliela101 (Posted by) Level 3
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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.

Approach - Data - Kernel - TextIQ - Corporations

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.

Status - Quo - Search - Terms - Hire

“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...
(Excerpt) Read more at: TechCrunch
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