AIStudio
Launchpad
Factory Management System
Agentic AI – GenAI Empowered 360° Operational Observability
MicroAI’s GenAI Agents provide 360° visibility, intelligence, adaptability, and security to optimize every layer of your machine ecosystem.
Agents
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Machine Intelligence
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Application Performance Monitoring
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Security and Monitoring
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Network Quality of Service
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Vision
Machine Intelligence
Machines that don’t just operate — they think.
Enables machines to learn, adapt, and make decisions locally. A transition from machine automation to machine autonomy.
Application Performance Monitoring
Turn on the lights for your running apps.
Monitor the health, responsiveness, and behavior of your applications in real time — with AI agents that optimize performance and uptime at the edge.
Security and Monitoring
AI-powered threat detection at the edge.
Monitor, detect, and prevent threats in real-time – all without cloud dependence. Lightweight, autonomous and always alert.
Network Quality of Service
Empowering 5G networks with Agentic GenAI.
Monitor, and optimize network performance in real-time, enabling proactive management rather than reactive troubleshooting.
Intelligence Vision
Detect More. Faster. Right at the Edge.
AI-Enabled visual Intelligence for real-time object detection and analysis. Always on. Always watching. Always learning.
Platforms
AIStudio
Transforming your data into foresight.
AI-enabled data modeling that transforms complex data into dynamic, predictive models — accelerating innovation from insight to action.
Launchpad
MicroAI Launchpad™ – A complete AI-enablement ecosystem that provides advanced AI capabilities with reduced cost and less complexity.
Factory Management System
An AI-Enabled, Edge-Based, Agent for Factory Optimization
Factory performance revolutionized. An AI-enabled plug-and-play solution that delivers predictive manufacturing, predictive maintenance, and improved OEE for manufacturers.
Reduce machine downtime by
50%
Reduce machine maintenance costs by
35%
Improve machine OEE by
15%
Improve factory output by
20%
Improve cyber threat response time by
75%
Driving Industrial and Network Optimization
with Agent-based GenAI.
Brownfield and Greenfield Use Cases.
From Automation to Autonomy
Utilizing GenAI to make machines and networks smarter, more autonomous, and more cost-effective.
Multi-Model Interfaces
Real-time ingestion of data from machines, sensors, and edge devices. Use of Large Language Models (LLMs) models and AI-enabled vision capabilities to generate insights and granular visibility.
Foundational Models & Fine Tuning
Fine-tuned on manufacturing data from maintenance logs, network data, CAD drawings, SOPs, and failure reports.
Digital Twin Integration
GenAI enhances virtual representations of assets by simulating what-if scenarios, generating anomaly hypotheses, and proposing control logic.
Conversational Agents
Embedded AI assistants help technicians, engineers, and operators interact with systems in natural language. Enables the generation of impactful insights that are easily consumed.
MicroAI GenAI Capabilities for
01
Predictive Maintenance
Transition from preventive to predictive maintenance based on analysis of historical failure modes and continuous GenAI pattern recognition within current performance trends.
02
Process Optimization
AI-enabled workflows continuously learn and self-adjust to keep manufacturing processes performing at optimum levels. Human intervention is significantly reduced.
03
Quality Control
Vision capabilities combined with real-time analysis of sensor data identifies performance faults, suggests potential root causes, and identifies potential solutions.
04
Digital Simulation
Generate CAD variations, simulate production lines, or test new product formulations digitally. Eliminates the need to shut down machines and lines to perform validation testing.
Learn More
01
Network Planning and Design
Generate optimized topologies, frequency plans, or capacity forecasts using historical data and intelligent forecast models.
02
Incident Detection and RCA
Generate root causes for alerts or outages and suggest remedial actions via rapid analysis of historical incidents. Reduces downtime and improves customer experience.
03
Traffic Optimization
Recommend load balancing, bandwidth allocation, or path rerouting in real time. Optimizes use of available bandwidth and ensures accurate allocation of resources.
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Anomaly Generation for Training
Create synthetic failure and anomaly data for training supervised models where real examples are rare. Enhances the ability to effectively respond to unusual network issues.
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Our Tech
Closed-Loop Asset Observability
- I/O Layer
- Auto - Tuning
- Health Scores
- Fault Detection
- Alerts
- Root Cause
- Corrective Action
I/O Layer
Auto - Tuning
Health Scores
Fault Detection
Alerts
Root Cause
Corrective Action
I/O Layer
Live data is leveraged from a variety of devices, machines, and networks. MicroAI’s technology is agnostic to sensor values and types, creating a multi-variant model that utilizes AI inference analysis to generate a wide range of analytics.
Learn MoreAuto - Tuning
Fully automatic tuning of the AI model(s) to be deployed. Multidimensional behavioral algorithms produce recursive analysis, training, and processing. This enables a continuous evolution of the AI model that takes place directly on the endpoint.
Learn MoreHealth Scores
Real-time, on-demand, health scores provide continuous observability into the health, performance, and security of connected assets. Stakeholders and operators can fast-track health assessments and to identify recurring problems based on historical data and predictive insights.
Learn MoreFault Detection
Embedded ML algorithms learn the normal operating behavior of an individual machine or a group of machines. Deep federated learning provides the accurate baselines required to rapidly detect performance anomalies of any size or duration.
Learn MoreAlerts
The embedding and training of intelligent workflows automate the process of performance alert notifications to ensure accurate dissemination of critical information. Alert routines can be customized to accommodate specific ecosystem configurations and requirements.
Learn MoreRoot Cause
High-speed processing of historical asset performance data enables rapid detection of historical patterns as well as analysis of relationships between complex variables impacting the performance of a machine or machine group. Root cause identification accuracy is improved, leading to faster recovery and reduced downtime.
Learn MoreCorrective Action
Through accurate identification of root cause, the algorithms will identify effective corrective actions to be implemented. Once implemented, the AI engine provides real-time impact assessments and self-tunes for maximum performance.
Learn MoreBusinesses putting their
trust in MicroAI
