---
id: engines/simile
about:
  label: Simile
  description: >-
    Company founded by the Stanford researchers behind generative agents that
    sells simulations of how customers, employees or populations would respond
    to a change, from models it says are trained on data about real people.
  tags:
    - simulated people
    - synthetic respondents
    - market research
    - LLM agents
    - commercial
tool:
  form: commercial hosted service
  group: People and populations
  homepage: 'https://www.simile.com/'
  cost: not published
open:
  source: closed
  data: closed
  api: unknown
  participation: unknown
domains:
  - people
relations:
  - predicate: built_on
    target: engines/generative-agents
  - predicate: alternative_to
    target: engines/aaru
provenance:
  origin: agent
  sources:
    - title: Simulating human behavior | Simile
      url: 'https://www.simile.com/'
    - title: >-
        LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of
        Individuals (arXiv abstract)
      url: 'https://arxiv.org/abs/2411.10109'
  created: '2026-10-23'
  reviewed: '2026-10-23'
  reviewed_by: 'Claude, from the sources listed'
  status: draft
---
Simile sells simulations of people. Its site says its simulations show "how and why customers, employees, or populations respond to change", such as a new product concept, a price or a message.[^1] The founders are Joon Sung Park (chief executive), who led the Stanford research on [generative agents](/engines/generative-agents/), Michael Bernstein (chief data officer) and Percy Liang (chief scientist), all from Stanford.[^1] The site announces "our Series B: $200M funding at a $2B valuation".[^1]

By the company's account, each simulated population starts from real people.[^1] Simile's algorithms design large studies daily, and the company combines the responses with its own datasets of human behaviour to train its models.[^1] It says it validates against real people weekly, with over 7,000 evaluations across subpopulations and real enterprise use cases, and that these feed a confidence model that tags every result with a predicted accuracy level.[^1] It says it re-collects data when it detects that the world has drifted from the model.[^1] Customers can bring their own data, such as loyalty data, balance histories or telemetry.[^1]

The site quotes people at CVS Health, Wealthfront, Banco Itaú, Suntory Beverages & Food, Gallup and Garnett Station Partners.[^1] It cites two research results from the founders' academic work.[^1] The first is the 2024 study of agents built from interviews with about 1,000 people, which it summarises as reproducing answers at 85 percent of the people's own consistency on a retest.[^1] The current version of that paper on arXiv gives 83 percent for agents built from interviews alone and 86 percent for interviews and surveys combined.[^2] The second is a 2025 paper on fine-tuning models with 2.9 million responses from 210 experiments, which it says improved alignment on unseen studies by 26 percent over the base model.[^1]

No price is published; the site's call to action is "Run a simulation".[^1] The weekly validation figures are the company's own; the site cites no independent evaluation of the commercial product.[^1]