A profile of Simile, which offers “agentic twins” modeled on real people to provide answers for polling and market research for companies like CVS and Gallup
AI startup Simile offers ‘agentic twins’ modeled on real people to provide answers for polling and market researchLinkedIn:Mike Volpi,Sri Narasimhan,Joon Sung Park, andAllegra Pedretti
Context & Ripple Effects
Simile’s profile follows its emergence from stealth with a $100M funding round to predict customer behavior, extending that positioning from purchase prediction into polling and market-research responses.
The company is part of a longer push to build digital twins from real-person data for uses including focus groups. Its work with CVS and Gallup puts that model inside established research and enterprise decision workflows.
First-order effects
- CVS, Gallup and other customers can use modeled respondents to generate research inputs without relying solely on conventional respondent panels.
- Simile’s product proposition shifts from predicting individual customer choices to supplying synthetic answers for broader polling and market-research tasks.
Second-order effects
- Research firms and corporate insights teams will face pressure to demonstrate when synthetic-panel outputs align with, supplement, or cannot replace responses from human samples.
- The value of consented source data and the controls governing how a person’s modeled likeness is used become more central to vendors serving research buyers.
Third-order effects
- If synthetic respondents prove reliable for defined use cases, market research could move toward hybrid designs in which human data calibrates models and models expand the volume of scenarios tested.
- That shift would make likeness governance a core procurement and trust issue, rather than a narrow feature of consumer-facing AI products.
The trend: AI is moving from conversational imitation toward enterprise systems that model people as reusable inputs to forecasting, research, and decision-making.