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Allegis Group invests in HiringSolved, an AI system that analyzes data in the hiring process

October 30, 2017

Allegis Group announced an investment in HiringSolved, a Chandler, Ariz.-based firm that makes artificial intelligence software that aims to use data — such as from applicant tracking systems, candidate relationship management software or human resource information systems — to analyze patterns and help match new hires with jobs, reallocate internal workers or help companies meet diversity goals. The system is also designed to streamline the process for recruiters.

“HiringSolved’s capabilities are game-changing for our growth strategy,” Allegis Group Global Development Officer Chris Hartman said in a statement, “and present great upward potential for enhancing how our industry engages the best talent required to get work done.”

HiringSolved’s products include RAI — Recruiter AI interface — that allows recruiters to access its software by speaking to RAI.

Founder Shon Burton said HiringSolved’s software is always learning and uses neural networks to get good at prediction, although he doesn’t use the term “AI” a lot. “It’s a lot like saying ‘database,” he said. “It’s a foundational technology.”

Allegis reported the investment last Tuesday. The size of the deal was not announced, but Burton said it was significant and provides the company with fuel for further development.

HiringSolved’s customers include Dell, GSK and Netflix.

In 2014, the company agreed to pay $40,000 to LinkedIn after the business networking site sued it for scraping data. Burton said the company is one of the few that fought that battle and survived, but it’s something the company worked through and learned from in terms of understanding how it will work with LinkedIn going forward.

However, a US judge in the separate case of LinkedIn vs. hiQ Labs ruled that LinkedIn cannot prevent hiQ Labs from scraping data from LinkedIn profiles, Reuters reported. HiQ Labs scrapes the data and analyzes it to, among other things, let companies know if any of their workers are likely to quit.