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Startups that promise to predict how humans will behave are having a moment. Over the past year, Simile raised $200 million<\/a> at a $2 billion valuation; Aaru raised $88 million<\/a> at a $1 billion valuation; and Humans&, an AI startup that announced a massive $480 million seed round<\/a> in January at a $4.48 billion valuation, launched Persimmon<\/a> to model human behavior.<\/p>\n The status quo for human behavior prediction today relies heavily on large language models (LLMs) that are prompted or fine-tuned to role-play as a target demographic. But two-year-old, San Francisco-based Mirror Particle<\/a> thinks that approach is fundamentally broken.<\/p>\n \u201cIt\u2019s like bringing a super soaker to Niagara Falls,\u201d says Abhivyakti Ahuja, co-founder and CEO of Mirror Particle, which provides brands with an AI engine that predicts consumer behavior and the reasons behind it. \u201cLLMs have been trained on hundreds of billions of data points. How much can you influence its behavior by [fine-tuning] with such a small amount of data? It\u2019s still stuck in the past.\u201d<\/p>\n Ahuja doesn\u2019t think LLMs see the world the way a human does. \u201cLLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence.\u201d Relying on them, she says, means getting insights based on what humans don\u2019t <\/em>notice, which is beside the point when trying to predict human behavior.\u00a0<\/p>\n Mirror Particle is taking another approach: building a foundation model, or as Ahuja describes it, a world model built from scratch that simulates why humans do what they do and how human behavior changes over time.\u00a0<\/p>\n \u201cWe don\u2019t want to capture the static person,\u201d Ahuja said. \u201cWe want to capture the changing person. That means capturing the longitudinal data on how people are changing, what triggers are changing them and to what degree.\u201d If they aren\u2019t changing, she added, \u201cthat\u2019s also a signal.\u201d<\/p>\n Mirror Particle has already raised an angel round and says it\u2019s close to closing its first venture round.\u00a0The company is also competing next week in Startup Battlefield 200<\/a>, TechCrunch\u2019s renowned startup competition taking place at TechCrunch Disrupt 2026<\/a> in San Francisco on October 13-15.<\/p>\n The startup relies on a proprietary combination of data that includes its clients\u2019 customer data, current events, pop culture, social media, and more to model a demographic segment, thinking of it as a system that evolves over time and tracking how motivations shift as it moves through experiences. Much of the focus is on \u201crevealed behavior\u201d \u2014 what people actually do rather than self-reported survey answers.<\/p>\n Like its rivals, Mirror Particle\u2019s initial go-to-market strategy focuses on where budgets already exist for these kinds of insights: market research and brand and product strategy. Mirror might, for instance, help a beauty brand not just write better ad copy for makeup that would appeal to Gen Z, but also determine if that demographic even wants that product.\u00a0<\/p>\n \u201cWhat if [the target demographic] doesn\u2019t want eyeshadow palettes?\u201d Ahuja said. \u201cMaybe blush is a better option to go for if you want to sell a product to this market.\u201d<\/p>\n Mirror Particle\u2019s prediction engine also provides customers with the \u201cwhy\u201d behind current or future behavior \u2014 the motivations, constraints, and additional context that justify its recommendation, helping brands make smarter decisions.\u00a0<\/p>\n In one early pilot, a well-known pet food brand wanted to know what imagery to put on the packaging to boost sales. Chicken? Beef? Vegetables? Mirror\u2019s technology found that the brand was asking the wrong question. The imagery didn\u2019t matter. The problem was that the brand was so recognizable that it was considered mass market and cheap, and sales would plateau until it addressed that perception issue.\u00a0\u00a0<\/p>\n \u201cThe way we see our model evolving is like how a baby learns about the world,\u201d Ahuja said, noting that babies move from vision to language to body awareness to social intelligence.\u00a0<\/p>\n That fundamental interest in modeling the human brain comes from Ahuja\u2019s background studying neuroscience and computer science. Originally from India, she ended up studying at the University of Toronto, where she became inspired by AI pioneer Geoffrey Hinton\u2019s contributions to neural networks.\u00a0<\/p>\n After school, Ahuja ended up at Amazon Robotics building robots that build other robots. That\u2019s where she met her co-founders, Will Song and Thomson Yen. Song has spent a chunk of his career building sales personalization engines, and Yen focused on using deep learning to learn about how AI agents understand human behavior.\u00a0\u00a0\u00a0<\/p>\n The startup\u2019s long-term vision is to be the \u201cgeneral layer for anticipating human behavior\u201d and moving from broader population-level analyses to individual-level insights.\u00a0<\/p>\n \u201cWe just need a better model of humans if we\u2019re going to work alongside AI and with each other,\u201d Ahuja said.<\/p>\n Check out Mirror Particle and many other innovative startups that have been vetted by TechCrunch\u2019s editorial team next week at Disrupt in downtown San Francisco<\/a>. The winner of this year\u2019s Startup Battlefield will be decided by our slate of VC judges on the afternoon of Thursday, October 15.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n