Egomic
Engineer & Idea Maker · 2026
Industrial SOP data layer for embodied AI. A platform that structures, annotates, and serves Standard Operating Procedure data to train and deploy robot agents in complex real-world environments.
Problem & Approach
Embodied AI and robotics models need high-quality, structured SOP data to learn complex multi-step tasks — yet no standardized data layer exists for this. Raw video and unstructured instructions are too noisy for robot training pipelines, and existing annotation tools weren't built with the precision that industrial SOPs demand.
Solutions & Impact
Egomic provides an end-to-end SOP data pipeline: a structured data format for encoding procedural knowledge, a rich annotation viewer for labeling robot-relevant actions with spatial and temporal precision, and an API layer that feeds clean SOP datasets directly into embodied AI training workflows. The platform enables frontier AI labs to scale data collection for manipulation, navigation, and dexterous tasks.
Learning & Reflection
Building Egomic pushed me into the intersection of robotics, data engineering, and AI infrastructure. I learned how to design annotation schemas that capture the nuance of physical tasks — contact events, object states, spatial relations — and how to build tooling that domain experts and researchers can both use effectively. It also taught me how to architect a data platform that must be both highly structured and flexible enough for a rapidly evolving field.