This category covers foundation models designed to understand human cognition, emotion, and behavior. Trained on neural, psychological, and multimodal signals, these models infer internal states like mood, attention, or intent. They enable emotionally aware agents, mental health tools, and brain-computer interfaces, forming a new intelligence layer centered on human inner states.

his category focuses on foundation models built for manufacturing and industrial systems. These models learn from sensor data, machine telemetry, and production video to support autonomy on the factory floor. They power use cases like control generation, quality inspection, fault detection, and process optimization across complex industrial environments.

This category covers startups building healthcare foundation models trained on clinical, imaging, and patient data. These models aim to support diagnosis, documentation, care coordination, and decision support across workflows. By combining text, images, and signals, they act as core medical intelligence layers rather than single task tools.

This category covers startups building foundation models that give robots general perception, reasoning, and control capabilities. These models learn from large-scale sensor, video, and simulation data to operate across tasks and environments. The goal is reusable robotic intelligence that transfers across hardware, settings, and applications rather than task-specific automation.

This post explores startups building foundation models trained on biological data such as DNA, RNA, proteins and cell states. These domain specific models act as core infrastructure for biotech research, enabling faster drug discovery, target identification and therapeutic design by learning general biological patterns from massive, multi-modal datasets rather than narrow, task specific models.