Layoffs continue to plague the tech world, but with the need for tech talent in organizations only growing, more emphasis is being placed on how to manage internal talent.
Call a startup from Ghent, Belgium TechWolf takes a unique approach to addressing this 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 more accurately assess the interests and skills of various employees, help connect them to different projects, and ultimately provide them with better training and more.
The company is making waves with its technology, boasting an impressive client list that includes GSK, HSBC, Booking.com and many others. And now it has raised nearly $43 million ($42.75 million, to be exact) in funding to expand its operations.
London-based Felix Capital is leading this Series B, with SAP, ServiceNow and Workday – three HR titans – co-investing alongside each other for the first time. 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 at around $150 million.
CEO Andreas De Neve, who co-founded TechWolf with Jeroen Van Hautte and Mikaël Wornoo, started the company in 2018 when all three were still computer science students at the University of Ghent in Belgium and Cambridge in England.
The original plan was to create an HR platform — with the startup building its own language model “like ChatGPT,” he said — to help source and hire talent from abroad.
“It failed,” he said simply. Recruiting, or at least the part of it they were trying to tackle, wasn’t all that broken. Employers “didn’t need artificial intelligence to filter the good applicants from the bad.”
But the founders discovered that their target customers had a different problem that needed to be fixed.
“They said, ‘Hey, this AI model, is there any chance we can use it on our 40,000 employees instead of our applicants?’ Because there might be people we could recruit internally,” De Neve said. “HR leaders pointed us toward the right problem to solve: identifying employee skills.”
The question “What do you actually do?” was a recurring joke about Chandler (an IT employee) on the TV show “Friends”. But it turns out to be a significant issue for real-world businesses, and it gets worse the bigger the organization gets. “You can have 100,000 employees who are all highly skilled, who all spend a lot of time on software systems that generate data,” De Neve said. “But structurally, these companies know very little about these people. So that’s what we started doing.”
That’s exactly the kind of problem that artificial intelligence 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 this data, we infer what skills these workers have. You can almost think of it as a set of AI models connected to the digital exhaust of an organization.”
TechWolf touches on a few important currents in the market right now that are worth noting:
- The real dilemma of innovators? The core book, “The innovator’s dilemma,” paints a compelling 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 it another way, the key asset that helps one organization operate more flexibly than another is its people: How easily teams can be formed around different projects and goals will arguably be what makes or breaks them. the efforts. And it turns out that organizations are willing to pay good money for technology that can help them with this task.
- LLM vs. MLM vs. SLM. The “big” language models, and the companies that build them, continue to generate enormous interest. And “generate” is really the operative word here, as it’s what underpins popular AI applications like ChatGPT, Stable Diffusion, Claude, Suno, and more. But there is certainly 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 limited and therefore less prone to hallucinations. TechWolf is not the only company working in this area, nor the only one catching the eye of investors. (Another example is the startup Poolside, which also builds AI for a specific use case: developers and their coding jobs.)
- Focus really does count. I asked De Neve if TechWolf had ambitions to leverage the platform to expand into other areas such as enterprise search or business intelligence. After all, it already absorbs so much corporate information, wouldn’t it be a simple step further to build more products around it?
Nonwas De Neve’s categorical response: “We can process data like no one else in the market, but we are extremely, extremely focused on solving the skills problem because there is already too much demand for us, right now, in the market where we operate . .”
At a time when it feels like there’s a lot of noise in the AI world, the focus rings a bell and could be one reason why investors are interested in companies like these.
Julien Codorniou, the Felix partner who led this deal, believes that TechWolf could outperform even much larger companies coming from other corners, such as AI-powered business 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 HR.”
