TechWolf has built an AI engine that ingests data from internal workflows to learn about the people doing that work.
© 2024 TechCrunch. All rights reserved. For personal use only.
Layoffs continue to buffet the world of technology, but with the need for tech talent in organizations only growing, there’s a bigger focus on how internal talent is managed.
A startup from Ghent in Belgium called TechWolf is taking a unique approach to addressing that need. It has built an AI engine that ingests data from internal workflows to learn about the people doing that work. This is then turned into data for managers and internal recruiters to assess various employees’ interests and skills more accurately, help connect them with different projects, and ultimately provide them with better training and more.
The company is making some waves with its technology, boasting an impressive list of customers that include GSK, HSBC, Booking.com and many others. And now it has raised nearly $43 million ($42.75 million, more precisely) in funding to expand its business.
London-based Felix Capital is leading this Series B, while SAP, ServiceNow and Workday — three titans in HR — are co-investing alongside each other for the first time ever. Other backers include Acadian Ventures, Fortino Capital Partners, Notion Capital, SemperVirens and 20VC, along with unnamed “AI leaders” from DeepMind and Meta. From what we understand, the startup is now valued around $150 million.
CEO Andreas De Neve, who co-founded TechWolf with Jeroen Van Hautte and Mikaël Wornoo, started the company in 2018 when the three were still computer science students at the University of Ghent in Belgium and Cambridge in England.
The original plan was to build an HR platform — with the startup building its own language model “like ChatGPT,” he said — to help source and hire talent from outside.
“It failed,” he said simply. Recruitment, or at least the part of it that they were trying to address, was just not that broken. Employers “didn’t need AI to filter out the good applicants from the bad.”
But the founders discovered that their target customers did have a different problem that needed fixing.
“They said: ‘Hey, so this AI model, is there any chance we could use it on our 40,000 employees instead of our applicants? Because there might be people who we could recruit internally,” De Neve said. “The HR leaders pointed us toward the right problem to solve: identifying the skills of employees.”
The question “What is it you actually do?” was a recurring joke about Chandler (an IT worker) on the TV show “Friends.” But it turns out to be a major issue in businesses in the real world, and it gets worse the bigger the organization becomes. “You can have 100,000 employees that are all super capable, who all spend a lot of time in software systems that create data,” De Neve said. “But structurally, these companies know very little about these people. So that’s what we set out to do.”
That is just the kind of problem AI can solve, he said. “We started building language models that integrate with the systems people use for work: project trackers, documentation systems for developers, research repositories for researchers. And from all that data, we infer what skills those workers have. You can almost think of it as a set of AI models that connect with the digital exhaust of an organization.”
TechWolf touches on a few significant currents in the market right now that are worth noting:
The real innovators’ dilemma? The seminal book, “The Innovator’s Dilemma,” paints a convincing picture of how even the most successful, large companies can be undone by smaller businesses that move more nimbly to respond to change. But looking at this a different way, the core asset that helps one organization work more flexibly than another is its people: How easily teams can be formed around different projects and goals arguably will be what makes or breaks those efforts. And it turns out that organizations are willing to pay good money for tech that can help them with that task.
LLM vs. MLM vs. SLM. “Large” language models and companies that are building them, continue to generate a huge amount of interest. And “generate” is really the operative word here, since they are what underpin buzzy generative AI applications like ChatGPT, Stable Diffusion, Claude, Suno and more. But there is definitely a rising tide for “smaller” language models that can be applied to very specific use cases, which are potentially less complex to build and operate, and ultimately more constrained and thus less prone to hallucination. TechWolf is not the only company working in this area, nor the only one to be catching the eye of investors. (Another example is the startup Poolside, which is building AI also for a specific use case: developers and their coding tasks.)
Focus indeed counts for a lot. I asked De Neve whether TechWolf had any ambitions to leverage the platform to expand into other areas like enterprise search or business intelligence. After all, it is already ingesting so much enterprise information, wouldn’t it be one simple step further to build more products around that?
Non, was De Neve’s flat answer: “We can process data like nobody else in the market, but we are super, super focused on solving the skills problem, because there’s too much demand for us already, right now, in the market where we operate.”
At a time when it feels like there is a lot of noise in the world of AI, focus rings like a clear bell and could be one reason why investors are interested in companies like these.
Julien Codorniou, the partner at Felix who led on this deal, believes TechWolf could outmaneuver even much bigger companies coming from other corners like AI-based enterprise search. “Doing one thing well can really pay off,” he said. “They don’t want to be Workday or ServiceNow. They want to be the Switzerland of the HR department.”
Leave a Reply