Network Quality of Service (NQoS). Endpoint and Edge AI solutions that improve the transparency, performance, and reliability of today’s diverse and complex IT and OT networks. Greater customer satisfaction at reduced cost.
MicroAI’s products and technologies provide the ability to dynamically manage network SLAs via the enablement of deep—real-time—observability of network performance across large volumes of endpoint devices and processes connected to the network.
Mission-critical machines within the manufacturing, telecom, automotive, and infrastructure segments are transformed from opaque entities into assets that are fully transparent. This transparency delivers deeper insights that produce predictive
A comprehensive service orchestration platform that provides intelligent network device lifecycle management, live network performance impact assessments, real-time SIM activation/management, and improved customer experience.
A Kubernetes-based platform that combines intelligent workflows, predictive analytics, and visualization into a single platform. Launchpad provides rapid development, testing, and deployment of fully customized AI/ML capabilities for companies in the manufacturing segment.
The year over year increase in connected devices and the data they produce has had a ripple effect on attaining and sustaining high levels of network performance. Network operators and service providers need new technologies to meet demands of networks that are increasingly large and diverse. Current hurdles to maximizing NQoS include:
Weak signals are still a problem within many networks. Root-cause identification is often impeded by the inability to determine if an individual occurrence is caused by a problem with the connected device or the network. This leads to delayed mitigation and poor customer experience.
Network operators have difficulty in quantifying the impact of suboptimal radio signals on the user experience. This lack of insight can lead to reactions that are too heavy or too light.
Heavy reliance on cloud infrastructures creates high cost burdens, slows the transfer of critical data (latency), and exposes data to cyber-attack. A more local data processing approach is needed.
By imbedding and training AI models directly into critical network components and processes, MicroAI’s NQoS provides network operators, engineers, and business stakeholders with the real-time insights required to push network performance to levels of reliability, predictability, and sustainability.
Live data is leveraged from a variety of network devices, machines, and processes. 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 predictive analytics.
By monitoring and processing data related to physical radio signals received by the UE as well as user experience related inputs (ping, jitter, loss), operators can quickly determine the level of impact on user experience. This data will generate deeper insights into customer satisfaction, behavior, and loyalty.
Presentation of real-time network performance data via user-friendly, customizable, drag and drop dashboards. Data is customized to meet the specific requirements of various operational and business stakeholders.
Processing of more data locally, at the endpoint or on the edge, significantly reduces the amount of data that must be transmitted and stored in the cloud. This approach reduces data handling costs and eliminates problems associated with data latency.
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