A San Franciscó-i Mirror Particle startup egy olyan új alapmodellt fejleszt, amely a hagyományos nyelvi modellekkel szemben nem szerepjátékkal, hanem az emberi viselkedés szimulációjával jósolja meg a fogyasztói döntéseket. A fejlesztők szerint a jelenlegi nagy nyelvi modellek nem képesek pontosan előrejelezni a viselkedést, mivel csak írott szövegeken tanultak, míg a döntéseinket a vizuális észlelés és a szociális intelligencia is irányítja.
Az eszköz a statikus adatok helyett a fogyasztók folyamatos változását követi, és a kérdőíves válaszok helyett a ténylegesen megvalósult cselekedetekre épít. A modell nemcsak azt mutatja meg, hogy mit fognak tenni a vásárlók, hanem a döntések mögötti miérteket, motivációkat és korlátokat is elemzi.
A startup elsősorban a piackutatás, valamint a márka- és termékstratégia területén nyújt segítséget a vállalkozásoknak. A Rebecca Bellan által bemutatott technológia jelenleg még nem érhető el nyilvánosan, a cég a TechCrunch Disrupt konferencián mutatkozik be a nagyközönségnek.
Az eredeti szöveg (TechCrunch AI)
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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 at a $2 billion valuation; Aaru raised $88 million at a $1 billion valuation; and Humans&, an AI startup that announced a massive $480 million seed round in January at a $4.48 billion valuation, launched Persimmon to model human behavior.
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 thinks that approach is fundamentally broken.
“It’s like bringing a super soaker to Niagara Falls,” 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. “LLMs 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’s still stuck in the past.”
Ahuja doesn’t think LLMs see the world the way a human does. “LLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence.” Relying on them, she says, means getting insights based on what humans don’t notice, which is beside the point when trying to predict human behavior.
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.
“We don’t want to capture the static person,” Ahuja said. “We 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.” If they aren’t changing, she added, “that’s also a signal.”
Mirror Particle has already raised an angel round and says it’s close to closing its first venture round. The company is also competing next week in Startup Battlefield 200, TechCrunch’s renowned startup competition taking place at TechCrunch Disrupt 2026 in San Francisco on October 13-15.
The startup relies on a proprietary combination of data that includes its clients’ 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 “revealed behavior” — what people actually do rather than self-reported survey answers.
Like its rivals, Mirror Particle’s 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.
“What if [the target demographic] doesn’t want eyeshadow palettes?” Ahuja said. “Maybe blush is a better option to go for if you want to sell a product to this market.”
Mirror Particle’s prediction engine also provides customers with the “why” behind current or future behavior — the motivations, constraints, and additional context that justify its recommendation, helping brands make smarter decisions.
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’s technology found that the brand was asking the wrong question. The imagery didn’t 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.
“The way we see our model evolving is like h
