AI startup nabs $100 million to help firms predict human behavior

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• Simile raises $100M to help companies predict human behavior with AI simulations
• Index Ventures led the round; Bain Capital Ventures and AI figures also invested.
• CVS Health tests Simile's tech for store stocking and display decisions.

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Artificial intelligence startup Simile has raised $100 million in a new round of funding to help companies predict human behavior, including by guessing items customers might purchase and questions that might get asked on earnings calls.

The financing, set to be announced Thursday, was led by Index Ventures, with participation from Bain Capital Ventures, A* and Hanabi Capital. Other notable AI figures, including Fei-Fei Li and Andrej Karpathy, also contributed to the round. Simile did not disclose a valuation.

Simile emerged from stealth this week after having spent the past seven months developing an AI model trained on interviews with hundreds of real people about their lives. The company also fed its system data on historic transactions and text from scientific journals focused on behavioral experiments.

The startup said it aims to use its technology to anticipate the decisions a person might make in any given situation. To do that, it builds simulations populated by AI agents that represent the preferences of real people.

The company’s novel approach may provide another option for companies beyond relying on focus groups. CVS Health Corp., for example, has been testing the service, according to Simile, to inform decisions about what items to stock up on and display in stores.

Simile also thinks its technology can help companies better prepare for questions that analysts might pose on earnings calls or predict how a particular corporate announcement may be received by analyzing prior calls and research.

The startup is led by co-founders Joon Park, Michael Bernstein, Percy Liang and Lainie Yallen, all of whom have academic ties to Stanford. Bernstein, in particular, is the co-author of the influential ImageNet project, which set a benchmark for computer vision technology.