How It Works
Crowdi is a predictive, AI-powered app testing platform where developers upload their prototypes, and thousands of AI agents interact with them like real users.
Developer Upload
Developers provide a prototype of their app: web app URL, mobile app link, or interactive prototype (e.g., Figma / React). Crowdi runs the prototype in a sandboxed environment to prevent damage to live systems. The platform automatically sets up the simulation environment, including staging databases, user accounts, and logging.
Agent Creation
Each agent is designed to act like a human user. Agents have personas, goals, memory, behavioral probabilities, and social tendencies. The combination of these traits creates agents with behavioral realism, capable of making decisions that resemble actual human users.
Simulation Loop
Observe: Agents read the current state of the app. Plan: The agent’s LLM-based planner determines actions based on persona, memory, and goals. Execute: Actions are performed via a sandboxed browser. Log & Update Memory: Agent outcomes are recorded. Repeat: The cycle continues until the session ends.
Ecosystem Simulation
Crowdi can run tens of thousands of agents simultaneously, creating a synthetic user base. This allows for emergent behavior observation, load and behavioral stress testing, and pattern discovery in UX, onboarding, and feature engagement.
Telemetry & Analytics
During and after simulations, Crowdi collects detailed event data: session events, funnel analytics, engagement patterns, bug reproduction, and emergent insights. All data feeds into dashboards and reports for developers.
Safety & Sandboxing
Agents only run in staging or isolated environments. No destructive operations on production databases. Actions are validated and rate-limited to prevent unrealistic activity. Synthetic simulations ensure security and reproducibility.
Scaling & Optimization
Crowdi uses a tiered fidelity system to scale efficiently: High-fidelity agents (~5–10%), Medium-fidelity agents (~30–40%), and Low-fidelity agents (~50–65%). This reduces computation cost while preserving predictive accuracy.
Feedback Loop & Iteration
Simulation results are validated against real-world A/B testing. Agent models are fine-tuned over time for better prediction. Developers iterate based on Crowdi insights, shortening product cycles and reducing risk.
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