Fountain occupies a large niche. The company, which just announced a $23 million funding round, provides a job recruiting and onboarding platform for gig, part-time, and hourly workers. Hiring these kinds of workers presents a different set of challenges from those faced by other job platforms, and Fountain is tapping into automation and machine learning to solve them.

“It’s a high-volume type of recruiting,” said Fountain CEO and cofounder Keith Ryu in an interview with VentureBeat, “high velocity, high turnover, and high unpredictability.”

Applying AI tools to hiring processes is typically tricky because it involves so many factors and requires so much intelligence. For example, ZipRecruiter’s Get Recruited platform uses a deep learning-based recommendation algorithm and natural language processing (NLP) to connect job seekers and employers. ZipRecruiter built multiple tools to do everything from intelligently helping job seekers punch up their resumes to conquering social engineering challenges, and it’s had to carefully address endemic hiring issues, like bias.

But Fountain has a different set of problems to solve for. It’s not especially difficult to find someone willing and qualified to work at Taco Bell, for instance, but those hires tend to move on rather quickly, leaving openings that need to be rapidly refilled. Whereas Get Recruited might help a company find someone who’s a perfect fit for a specialized role, Fountain could be tasked with finding 400 people who know their way around a fast food kitchen — by next week.

Fountain needs speed and efficiency, but not necessarily a tremendous amount of nuanced insight. “We’re a talent acquisition platform, built specifically for this gig and hourly workforce — a global workforce, if you will, and our thesis is that recruiting for this demographic is a fundamentally different problem than recruiting for your internal sales or engineering roles,” said Ryu.

Ryu said some 90% of candidates in these areas don’t come in with a resume, which means employers end up starting the process from scratch. And even once they get past that point, interviews have a 50% no-show rate. He said 40% of applications ghost their potential employer after the first application.

fountain product sample

The gig and hourly job space comes with other unique challenges. Ryu pointed out that most of these applicants aren’t drawn by a company name, as a software engineering candidate might be. “[In] our environment, it’s less about … the brand or loyalty, and it’s more about work availability and proximity of your location,” he said. And when it comes to providing the best tools for applicants, Fountain had to invest in creating a mobile-first approach to its platform because, Ryu said, “80% of the candidates that we see are applying to these jobs on their mobile phone.”

At the same time, Ryu made it clear that Fountain wants to preserve the human side of the process. “How do you automate a process like recruiting, where it’s supposed to be so human? Our view … is that we believe there [are] a lot of tasks that are just kind of mundane … where there’s a lot of repetitive process[es] … that we want to automate. But then we also want to make sure that the recruiting team is able to spend more time with the candidates, as well.”

Ryu said the Fountain team pulled from their experience in marketing automation software, which comprises workflow automation and lots of data, to build the service. Companies come to Fountain with requests like “We need to hire 30 drivers” by a certain date. Fountain uses its algorithms and some machine learning to determine what that task will cost the company and then automates parts of the recruiting process, like figuring out the best job boards to post on.

Once candidates start coming in the door, Fountain uses its own models to prioritize them, based on multiple factors — like how long it takes someone to move from one stage of the application process to another, whether they have missed any previous interviews, and so on. Then it advances the candidate accordingly.

Fountain has an iterative training process, and Ryu said it can actually provide customized insights by letting customers tap into their own post-onboard data, like conversion rates.

Fountain is fresh off a $23 million funding round, led by DCM, with participation from 51job and previous investors Origin Ventures and Uncork Capital. With this fresh influx of cash, Fountain has raised a total of $34 million. Ryu said the company plans to invest in R&D, build out its products, and keep pushing into new markets — specifically in the restaurant and hospitality fields.

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