Research
Hyperplane Labs develops foundational infrastructure for AI systems that can understand, learn from, and operate within complex environments, unlocking machine-human collaboration.
Company World Model
AI research exploring how companies actually operate and developing a model that thinks like an entire company as a single autonomous actor.
Memory Systems
Current AI systems lack persistent, contextual memory. We research architectures that allow AI to maintain and reason over long-term context while preserving privacy and computational efficiency.
Secure Agent Runtimes
As AI agents become more autonomous, secure execution environments are critical. We develop runtime systems that enable AI agency while maintaining strict security boundaries and user control.
Human-Level Document Understanding and Perception
We research novel and robust multi-modal extraction and parsing systems for complex documents, turning unstructured, rich and varied data into structured, machine-readable outputs. Our focus is production-grade accuracy and human-level perception that enables reliable downstream AI applications while preserving privacy and handling real-world document complexity.
Reinforcement Learning Environments
AI agents require rich, persistent environments to learn complex behaviors. We research RL environments that enable agents to develop genuine understanding through interaction with realistic, long-horizon tasks in personal computing contexts.