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Workera Raises $23.5M Series B Funding

Workera

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Workera, an upskilling platform focused on enterprise customers, has raised $23.5 million in a Series B funding round led by Jump Capital, with participation from NEA, Owl Ventures, the AI Fund, and Sozo Ventures.

The company plans to use the fresh capital to expand its product offerings and grow its developer team.

Workera measures skills in the workplace and offers personalized learning, providing employees with the role- and goal-specific learning plans and enabling companies to measure skills and create custom upskilling plans.

Workera claims to be different from other skilling platforms due to its data science-forward approach, using a “skills dataset” with millions of measurements across over 7,000 skills to train AI algorithms, allowing it to understand the relationship between skills and measure more skills in less time.

Workera also plans to launch a skills and assessment authoring platform later this year, embedded with AI tech to automate the process of creating new assessments.

According to a 2022 PwC survey, 40% of companies are actively upskilling their employees as they look to automate or enhance work through tech. VCs have taken note of the opportunity, investing billions in a variety of companies in the skilling space over the past year. Workera is an unabashed beneficiary of this boom, with Samsung, Siemens Energy, the U.S. Air Force, and Belcorp among its current customers.

Before launching Workera, CEO Kian Katanforoosh and Workera’s chairman, Andrew Ng, taught AI classes at Stanford and DeepLearning.ai, Ng’s e-learning venture. Katanforoosh says he came to realize that people weren’t starved for upskilling content, but instead were missing the guidance and mentorship necessary to increase their skills.

Workera’s product aims to put this into practice by measuring skills in the workplace and offering personalised learning. Using generative AI, the platform creates questions for skills assessments and also taps AI to summarise information about a skill for users who have just enough proficiency that a full course wouldn’t make sense.

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