OpenHuman provides an AI-powered platform that simulates customer behavior to help companies optimize commercial decisions before launch. The platform lets users explore offer structures, pricing tiers, and marketing plans through natural-language queries, with autonomous agents testing thousands of combinations and generating pull requests for implementation. It goes beyond traditional analytics by explaining why customers behave the way they do, not just what happened.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Marketing Technology
- Sales Technology
- Software Only
Funding
Funding not disclosed
Founders
Product
Problem
Companies often make commercial decisions—such as pricing, offers, and marketing plans—based on intuition or historical data that only reveals what happened, not why. This leaves them unable to predict how customers will react to new strategies, leading to missed revenue opportunities and costly trial-and-error in the real world.
Solution
OpenHuman provides a multi-agent world modeling platform that simulates human behavior at scale using LLM-powered autonomous agents. The platform creates a personalized, calibrated corporate simulation where users can explore, understand, and act on their customer ecosystem before it reaches the real world. Users can experiment with subscription lengths, pricing tiers, highlighted plans, and trials, simulating thousands of combinations to discover the best-performing offer. OpenHuman also autonomously explores thousands of possible decisions, identifies strategies most likely to achieve goals, and can even create a pull request for the team to review. Beyond what happened, the platform explains why—answering natural questions like why a discount didn't increase revenue or which objections prevent conversions.
Target Audience
Primary customers are commercial teams, including pricing, product, and marketing leaders at companies who need to optimize offers, pricing, and go-to-market strategies before launching to real customers.
Features
- Autonomous optimization that explores thousands of possible decisions and recommends strategies most likely to achieve goals
- Natural-language query interface that explains customer behavior, such as why a 30% discount failed to increase revenue
- Calibrated corporate simulation with archetype-level insights, including conversion breakdowns by customer segment
- Offer structure experimentation covering subscription lengths, pricing tiers, highlighted plans, and trials
- Implementation support that generates pull requests for teams to review and deploy recommended changes
- Multi-agent world modeling using LLM-powered autonomous agents to simulate human behavior at scale