Three Core AI Systems
Built for the Long Term
NeuronetAI is developing a modular AI architecture composed of three tightly integrated systems: an AI agent for orchestration, a vision system for perception, and a generative reasoning model for clinical decision support research.
Our AI System Architecture
T-Rex — AI Agent & Orchestration
T-Rex is a personal AI agent responsible for reasoning, task coordination, tool execution, and interaction with other AI systems. It acts as the central intelligence layer connecting perception, knowledge, and action.
T-Eye — Vision & Detection Models
T-Eye is a vision system built on YOLO-based models and future multimodal architectures. It focuses on extracting structured signals from images to support clinical image review and visual pattern recognition.
T-Brain — Generative Reasoning Models
T-Brain consists of generative and language models designed to understand medical context, summarize risks, and assist clinical reasoning without providing diagnosis or treatment.
Training & Deployment Strategy
Local & CPU-Based Systems
T-Rex primarily runs on CPU-based environments for orchestration, reasoning, and personal research usage, prioritizing stability, privacy, and long-term maintainability.
GPU-Based Training
T-Eye and T-Brain are trained and evaluated on GPU-backed infrastructure to enable efficient vision learning and generative model experimentation.
Modular Deployment
Each system is deployed independently and communicates via secure APIs, allowing models to evolve without rebuilding the entire platform.
NeuronetAI systems are designed for research and decision support. They do not provide autonomous diagnosis or medical treatment. All outputs are advisory and must be interpreted by qualified professionals.