MicroAI is a pioneer in Agentic Generative AI solutions, engineering platforms and products that integrate **edge-based intelligence** and **autonomous decision-making** to create a foundation for operational resilience, efficiency, and growth for IT and OT assets globally.
MicroAI provides next-generation Edge and Embedded AI/ML products designed to deliver operational value in **machine and network optimization, predictive maintenance, and advanced cyber-security** (2.3). The company was founded to address the critical gap in enterprise digital transformation where data is abundant but autonomous, actionable intelligence is lacking. MicroAI solves this by pushing GenAI intelligence directly to the edge, enabling machines to **self-monitor, self-heal, and self-optimize** (3.1).
By embedding intelligence directly onto microcontrollers (MCUs), MicroAI bypasses the latency, security concerns, and high cost associated with cloud-first AI models (1.7). This delivers real-time insights and localized control for both IT and OT environments.
MicroAI’s core technology is built around AtomML™, a proprietary, lightweight AI engine that enables self-learning and inferencing directly on smart assets.
The MicroAI solution is a complete ecosystem, providing the engine for endpoint AI, the platform for enterprise management, and the tools for model creation (1.7, 2.3).
MicroAI delivers a portfolio of solutions focused on maintaining asset health, ensuring network performance, and fortifying security.
Transforms assets from opaque entities into intelligent agents, providing a real-time Health Score based on sensor data. This enables the transition from reactive to **predictive maintenance** (1.3, 2.3).
AI-enabled closed-loop cyber security and monitoring for IT and OT infrastructures. It provides endpoint protection by visualizing disturbances and detecting **zero-day events** at the asset level (2.3, 2.6).
AtomML™ is designed for resource-constrained devices, allowing full AI training and inferencing directly on microcontrollers with minimal memory requirements (2.6).
The AI models learn and evolve automatically on the edge device itself, continuously adapting to normal operational behavior and instantly flagging anomalies (2.6).
Local data processing drastically reduces the need for constant data transfer to the cloud, cutting connectivity costs and eliminating reliance on large infrastructure (2.3, 2.6).
Provides deep, real-time visibility into the performance, health, and security status of every machine and connected asset within the network (1.7, 3.1).