Many critical assets (machines, devices, network equipment, etc.) lack the AI-enabled observability required to produce deep insights into asset performance, health, and security. The lack of deep, asset-centric, AI-enabled observability creates a host of problems within the asset ecosystem:
Asset ecosystems are plagued by a host of problems resulting from legacy solutions that watch but that do not actively learn.
The lack of deep asset observability inhibits the gathering of insights into the effects of varying environmental conditions and their impacts on asset performance.
Asset fault detection is delayed, creating trickle-down effects on performance, productivity, and security.
The inability to generate timely, accurate, actionable, predictive analytics is a prohibitive limitation in fully optimizing the performance and security of mission-critical assets.
embedded directly into the MCU/MPU of an asset
and training based on real-time asset conditions
and inferencing that occurs at the endpoint
that enable predictive maintenance and health scores
that requires little to no labeled data
into an existing IT and OT ecosystem
MicroAI’s endpoint asset observability methodology provides breakthrough advantages for any company dependent upon high performance and reliability of IT/OT assets.
