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UG Labs raises $7M to infuse conversational AI into games for kids

Mar 11, 2024 - venturebeat.com
UG Labs, an Israel-based company specializing in conversational AI for children's games, has raised $7 million in a funding round led by MoreVC, with participation from Amiti Venture, Mediatek, and private investors. The company's technology uses proprietary algorithms and data to create open-ended conversations within games that can dynamically affect gameplay. The core technology revolves around automated speech recognition (ASR) and large language models (LLMs) that learn and adapt to the user's speech patterns over time, improving understanding and inclusivity.

Founded in 2022 by CEO Ariel Leventhal and CTO Shachar Mendelowitz, UG Labs has raised a total of $23 million to date. The company's technology can also be integrated into connected toys, enhancing user engagement and retention. The company also emphasizes safety, recognizing inappropriate language and ensuring conversations adhere to predefined safety guardrails set by the game publisher. UG Labs is also working with regulator kidSAFE to ensure compliance with COPPA, the U.S. law that protects kids in online communications.

Key takeaways:

  • UG Labs has raised $7 million in funding to develop conversational AI and voice interactivity for children's games. The funding round was led by MoreVC, with participation from Amiti Venture, Mediatek, and private angels.
  • The company's technology uses proprietary algorithms and data to enable open-ended conversations that dynamically affect gameplay, offering unscripted and on-the-fly exploration with non-player characters.
  • UG Labs' core technology revolves around automated speech recognition (ASR) and large language models (LLMs), which are designed to understand and adapt to children's speech patterns and language command.
  • The company has also launched Kids Labs in the U.S., where it tests voice-led conversational designs and experiences with young users. The insights gained from this initiative have been crucial for ongoing iterations and improvements to UG’s ASR models.
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