The Seattle company develops a machine-learning program that reads through job postings and recruiting emails to help businesses be more effective in attracting candidates.
A Seattle startup has created a text-analyzing technology that may help the tech industry address its diversity and recruiting problems.
Textio, founded last year by Microsoft veterans Kieran Snyder and Jensen Harris, develops a machine-learning program that can automatically read through job postings and recruiting emails and tell a business how effective the writing will be for attracting candidates.
The company announced an $8 million round of funding Wednesday, bringing its total to $9.5 million. The latest round was led by San Mateo, Calif.-based Emergence Capital, with participation from Cowboy Ventures, Bloomberg Beta and Upside Partnership.
The technology, which can be tested free online, automatically scans through a job posting, then spits out a score up to 100. Below the score, Textio points out good and bad things in the posting and identifies whether the tone appeals more to men or women.
Most Read Stories
- A Washington county that went for Trump is shaken as immigrant neighbors start disappearing VIEW
- Seattle hits record high for income inequality, now rivals San Francisco
- Seahawks' Kam Chancellor likely out for season, report says, but Pete Carroll says nothing official yet WATCH
- Anthony Bourdain brought 'Parts Unknown' to Seattle — here's where he ate
- Kickoff time, TV info announced for 110th Apple Cup
Textio is used largely by technology companies, which have been criticized for employing mostly white men in their ranks, while underemploying women and minorities. The startup says its system can identify words that apply specifically to men and women and tell companies whether their postings are slanted to either gender.
Textio also says its system helps companies hire positions in 20 percent less time because it helps craft language that gets more qualified people to apply.
“The people that apply actually do much better on their interviews,” Harris said. “It gets more people in and more people from underrepresented groups. Roles are filling much faster.”
Textio’s team, which now has 11 people, spent the past year developing and testing its machine-learning and natural language-processing program. They fed the program information from a “huge database,” Harris said, referring to documents containing job listings and recruiting emails. Also included was information about which candidates were hired and how they performed at the company.
Textio’s customers include heavy hitters such as Twitter, Microsoft, Thomson Reuters and Starbucks. Companies pay a monthly subscription per user for the service.
Textio uses the data and documents it inputs from customers to constantly teach the program to better understand and analyze language.
After starting with job postings and recruiting emails, Textio plans to expand to performance reviews, marketing material and pretty much any company document.
Textio plans to use the latest funding to double staff and continue building the product, Harris said.