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Simulating the
World's Intelligence
Patronus AI develops simulation research and infrastructure to accelerate progress toward human-aligned AGI
Research-Backed,
Real-World Inspired
We translate our research into carefully calibrated environments that capture real human workflows. The result is a "Goldilocks Zone": the tasks in the environment are the perfect difficulty level for frontier models to learn from effectively.
These environments are powered by Generative Simulators, which jointly co-generate tasks, world dynamics, and reward functions. Developed by our research team, Generative Simulators scale high-quality environment creation.
We believe that generative simulators constitute the foundational infrastructure for self-adaptive worlds, extending beyond RL algorithms and human-curated datasets.
10k+ expert contributors
Across software, academia, finance, and more
1M+ world data artifacts
Covering diverse domains
85% UI/UX feature parity
With real-world products
15–25% model lift
Measured on long-horizon tasks
Research Science
Exploring research comprehension, synthesis, and extension
Software Development
Delivering software across many tools, services, and frameworks
Customer Service
Handling complex customer support cases end-to-end
Product Applications
Navigating UI/UX across web and mobile applications
Finance
M&A, private equity, quantitative trading, and strategic finance
Simulation Domains
Mirroring real human work across key industries and functions
Simulation
Capabilities
Designing scenarios that target core model skills and uncover brilliant learning signals
Deep Research
Understanding and reasoning over large semantic datasets
Multi-Turn Dialogue
Collaborative problem solving or chit-chat in a dialogue setting
Long Horizon
Task planning and execution that spans days to months
Memory
Agentic memory with context windows and other tooling
Featured Research
SOTA hallucination detection model
Lynx is the first model that beats GPT-4 on hallucination tasks
Lynx (70B) achieved the highest accuracy at detecting hallucinations
Industry-first benchmark for LLM performance on financial questions
High-quality, large-scale set of 10,000 Q&A pairs
Based on publicly available financial documents
Evaluate agent effectiveness in tip-of-the-tongue moments
Identify something a person can vaguely remember not name
Curated, high-quality dataset with 573 tip-of-the-tongue Q&A pairs
Evaluation model that produces high-quality reasoning chains and highlights
Can help make its decisions more explainable
Cost-effective for companies requiring efficient, fast, and reliable guardrails
