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Building on our research, we develop products that bring generative AI into practical use. Interview AI draws out how individuals think and what they value, while consumer, economic, and urban digital twins simulate society.

Consumer Digital Twin

We build consumer digital twins from interview, behavioral, and other data.

  • Run surveys and interviews with consumer digital twins around the clock at low cost
  • Provide custom digital twins optimized for each organization's data
Concept Highlights
  • Recreates consumers from interview, behavioral, and other data
  • Runs surveys and interviews with consumer digital twins around the clock
  • Provides custom digital twins optimized for each organization's data
  • Validates qualitative and quantitative hypotheses at low cost

Economic Digital Twin

We build societies of many AI agents from Interview AI personas and observe social phenomena.

  • Generate virtual societies with large numbers of agents
  • Simulate the impact of policies and rule changes
Concept Highlights
  • Generates personas automatically from interviews
  • Builds a virtual society from many AI agents
  • Simulates the impact of policies and rule changes
  • Analyzes and forecasts social phenomena quantitatively
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Simulacra Digital Twin

Multi-Agent Society Simulator
In development
InvestorExecutiveConsumerResearcherRegulatorMediaWorkerFounderPolicymakerEngineer
A-05→A-08 propagating information…

Persona generation

Using the personality traits, values, and decision patterns extracted by Interview AI, we automatically generate a diverse set of AI agent personas.

Social simulation

We place the generated agents in a virtual environment and reproduce social phenomena such as economic behavior, information diffusion, and consensus formation.

Policy impact analysis

We introduce policies, price changes, and institutional designs into the virtual society and evaluate their ripple effects and risks quantitatively.

© Simulacra Inc. (concept preview)
Multi-Agent Simulation Engine

Urban Digital Twin

We place LLM agents on 3D city models to recreate a living city where they converse, shop, and move.

  • Simulate realistic pedestrian flow and movement with around 100 LLM agents
  • Validate behavioral changes ahead of disasters and peak tourist seasons
Concept Highlights
  • LLM agents act autonomously on 3D city models
  • Recreates pedestrian flow through conversation, shopping, and movement
  • Validates behavioral changes ahead of disasters and peak tourist seasons
  • Visualizes the impact of urban planning and policies before implementation

Interview AI

An AI interviewer built for creating individual digital twins. Through conversation, it draws out a person's thinking, values, traits, and tacit knowledge.

  • An AI interviewer designed to surface how a person thinks and what they value
  • Puts tacit knowledge into words through the causal relationships the person perceives
Demo Highlights
  • Extracts thinking and values automatically from conversation
  • Puts tacit knowledge into words via the person's own causal reasoning
  • Builds and visualizes causal chains automatically
  • Structures the information needed to generate a digital twin

Simulacra AI Expert Interview

Interview AI • Finance
Recording
INTERVIEW

AI Interview

The AI draws out thinking, values, and tacit knowledge through dialogue

REC 00:12:48
AI
AI Interviewer
10:03

When you make an investment decision, what information and indicators do you look at first?

EX
Expert
10:04

I start with the direction of rates, prices, and the economy, plus corporate earnings trends. In the short term I watch supply and demand and spreads; over the long term, whether profit growth holds up.

AI
AI Interviewer
10:06

When rates or FX move sharply, how does that change your judgment?

EX
Expert
10:07

If rates rise, the discount rate goes up, so I pull back on growth names. If the yen weakens, I look at exporters' profits and adjust the weighting. When credit spreads widen, funding costs rise and capex slows, so I turn cautious.

Type your next question (voice or text)… e.g. further causal chains, observable indicators, possible interventions
CAUSAL

Causal model

Extracting and structuring the individual's causal reasoning

Causal network (extracted)Causal Graph
Rates↑Discountrate↑PER↓PriceWeakJPYExportmargin↑Earnings↑CauseEffectIndirect
Simulacra • Causal Digital Twin Pipeline
Session ID: FIN-VOICE-2026-01