Machine Lattice builds populations of data-grounded agents that simulate how real people and on-chain wallets will react to business decisions before they go live. Users describe a target audience in plain language, sample a synthetic population grounded in demographic or wallet behavior data, and run scenarios to generate decision-grade evidence. The platform is built for product, growth, marketing, policy, and market research teams across Web2 and Web3 sectors.
Funding
Funding not disclosed
Founders
Product
Problem
Companies often make product, pricing, and policy decisions without reliable evidence of how their target audience will react. Traditional market research is slow, expensive, and limited in scale, while testing on live customers risks brand damage and lost revenue.
Solution
Machine Lattice creates populations of data-grounded agents that simulate how real people and on-chain wallets behave across different scenarios. Users describe their target population in plain language, and the platform samples synthetic agents grounded in nationally representative demographic data or real on-chain wallet behavior. Each agent reacts to proposed product changes, pricing, messaging, token designs, or governance decisions, producing aggregate outcomes such as support, churn, or adoption rates. The system delivers decision-grade evidence before a change is deployed, allowing teams to pressure-test strategies with quantitative confidence.
Target Audience
Product and growth teams, marketing and brand professionals, policy and public researchers, market researchers, protocols and DAOs, and founders and strategy teams across both traditional and Web3 sectors.
Features
- Plain-language population descriptions that translate into agent samples without requiring technical expertise
- Two population universes: nationally representative demographic data for human agents, and real on-chain wallet behavior data for crypto-native agents
- Scenario simulation covering pricing, features, messaging, policy, token design, governance, incentives, and treasury decisions
- Wallet-level grounding in actual trading, holding, liquidity provision, governance, and stress-response behaviors across market cycles
- Quantitative evidence output with metrics such as percentage positive support and churn rates across evaluated agent cohorts