$ sim-in-silico --mode production --agents 24500_

SIM-IN-SILICO

SIMULATE • VALIDATE • DEPLOY

AGENTS0
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STATUSONLINE
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SIMS12 ACTIVE
AGENTS ONLINE2,847|SIMS/HR14.2K|AVG CONVERSION+4.2%|CHURN RATE2.8%|P-M FIT SCORE0.73|ACTIVE EXPERIMENTS12|MEM UTILIZATION67%|NET SENTIMENT+0.42|AGENTS ONLINE2,847|SIMS/HR14.2K|AVG CONVERSION+4.2%|CHURN RATE2.8%|P-M FIT SCORE0.73|ACTIVE EXPERIMENTS12|MEM UTILIZATION67%|NET SENTIMENT+0.42|AGENTS ONLINE2,847|SIMS/HR14.2K|AVG CONVERSION+4.2%|CHURN RATE2.8%|P-M FIT SCORE0.73|ACTIVE EXPERIMENTS12|MEM UTILIZATION67%|NET SENTIMENT+0.42|
OVERVIEW

Pre-Market Validation at Scale

An innovative platform combining agent-based modeling, behavioral economics, and large-scale multi-agent simulation. We've built a digital sandbox where thousands of AI agents simulate real consumer behavior, market dynamics, and product adoption patterns.

SIM-IN-SILICO — PLATFORM DEMO1 of 1
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Markets are complex adaptive systems. Traditional A/B testing reaches real users, takes weeks, and only tests surface-level variations. Agent-based simulation lets you test fundamental product hypotheses before writing a single line of production code.

Sim-In-Silico models realistic consumer populations with diverse personalities, preferences, and social dynamics. Agents form opinions, share recommendations, respond to pricing, and churn — just like real users.

This is an experiment in pre-market validation — a prototype system exploring how synthetic populations can predict real-world adoption curves, identify product-market fit signals, and stress-test go-to-market strategies.

Each agent carries its own memory, social graph, and decision-making architecture. They don't just follow rules — they reason, reflect, and adapt based on experience.

CAPABILITIES

Core Features

Purpose-built for product teams who want to validate before they build.

PRE-MARKET VALIDATION

Test product hypotheses, pricing strategies, and go-to-market plans against simulated populations before risking real resources. Get signal on product-market fit in hours, not months.

COGNITIVE AGENT MODELS

Each agent uses LLM-powered reasoning with memory, reflection, and social influence. Agents form genuine preferences, share word-of-mouth, and make nuanced adoption decisions.

SOCIETY SIMULATION

Construct and observe large-scale agent societies with realistic social networks, information propagation, and emergent market dynamics. Watch adoption cascades unfold in real time.

ARCHITECTURE

Agent Architecture

Three-layer cognitive architecture powering each simulated agent.

WORLD LEVEL

  • >Market environment with configurable products & channels
  • >Social graph modeling real network topologies
  • >Information propagation & word-of-mouth dynamics
  • >Economic feedback loops & competitive effects

INDIVIDUAL LEVEL

  • >Persona-based character profiles (demographics, values, goals)
  • >Episodic & semantic memory systems
  • >Social relationships with trust & influence scores
  • >Long-horizon behavioral history tracking

NEURON LEVEL

  • >LLM-powered planning & reasoning modules
  • >Reflection & self-evaluation cycles
  • >Contextual decision-making with memory retrieval
  • >Adaptive policy learning from outcomes
FAQ

Q&A