[NEWS] TextIQ, a machine learning platform for parsing sensitive corporate data, raises $12.6M – Loganspace

TextIQ, a machine studying machine that parses and understands sensitive company 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 challenge titled “Social Community Extraction From Text.” The algorithm he constructed used to be ready to learn a original, cherish Jane Austen’sEmma, for instance, and perceive the social hierarchy and interactions between characters.

This people-centric technique to parsing unstructured data within the fracture grew to become the kernel of TextIQ, which helps companies glean what they’re buying for in a sea of unstructured, and highly sensitive, data.

The platform started out as a instrument primitive by company felony teams. Attorneys normally must manually stare by draw of troves of paperwork and conversations (text messages, emails, Slack, etc.) to glean express evidence or data. Even the utilization of search, these teams exhaust hundreds of time and sources taking a stare by draw of the search results, which normally aren’t as simply as they have to be.

“The location quo for right here’s to make exhaust of search phrases and hire a entire bunch of people, if no longer thousands, to stare for things that match their search phrases,” said Agarwal. “It’s big costly, and it is going to clutch months to fight by draw of millions of paperwork. And it’s serene dangerous, because they could very properly be lacking sensitive data. In contrast with the location quo, TextIQ is no longer handiest more inexpensive and sooner nonetheless, most interestingly, it’s far more simply.”

Following success with felony teams, TextIQ expanded into HR/compliance, giving companies the ability to retrieve sensitive data about inner compliance concerns with out a manual search. On tale of TextIQ understands who an particular particular person is relative to the relaxation of the group, and learns that group’s ‘language’, it is going to more thoroughly extract what’s linked to the inquiry from all that unstructured data in Slack, email, etc.

Extra no longer too long ago, within the wake of GDPR, TextIQ has expanded its product suite to work within the privacy realm. When a firm is asked by a buyer to rating rating admission to to all their data, or to be forgotten, the map can clutch an unlimited quantity of sources. Even then, bits of data could tumble by draw of the cracks.

As an instance, if a buyer emailed Buyer Provider years ago, that could no longer attain up within the firm’s manual search efforts to glean all of that buyer’s data. However since TextIQ understands this unstructured data with an particular particular person-centric draw, that email wouldn’t stir by its machine, in accordance to Agarwal.

Given the sensitivity of the data, TextIQ features within the again of a firm’s firewall, meaning that TextIQ simply offers the instrument to parse the data in situation of taking on any felony responsibility for the data itself. In other phrases, the skills comes to the data, and no longer the unsuitable skill around.

TextIQ operates on a tiered subscription model, and offers the product for a fraction of the associated payment they give in savings when customers swap over from a manual search. The firm declined to allotment any extra little print on pricing.

Dilapidated Apple and Oracle Overall Counsel Dan Cooperman, extinct Verizon Overall Counsel Randal Milch, extinct Baxter Worldwide World Overall Counsel Marla Persky, and extinct Nationwide Insurance protection Chief Apt and Governance Officer Patricia Hatler are on the advisory board for TextIQ.

The firm has plans to dart on a hiring spree following the original funding, taking a stare to own positions in R&D, engineering, product pattern, finance, and sales. Cofounder and COO Omar Haroun added that the firm achieved profitability in its first quarter entering the market and has been winning for eight consecutive quarters.