The explosion in devices and machines connected to broadband and fixed wireless networks has created new opportunities and challenges for telecom operators.
Development of new offerings will require AI and ML solutions that are more personalized and that operate much closer to the assets within the telecom network. This edge and endpoint intelligence will maximize the performance, reliability, and security of these new offerings.
The ability to optimize, innovate, and disrupt in today’s hyper-competitive telecom market will require breakthrough solutions to several historical limitations.
AI enabled, decentralized, command and control is needed to aggregate, synthesize, and react to the massive amounts of data generated within the telecom network.
The lack of embedded intelligence makes it difficult for operators and device OEMs to acquire the real-time performance insights that would improve quality of service (QoS) performance to their customers.
The absence of multidimensional behavioral algorithms to produce the recursive analysis, learning, training, and processing required to enable predictive modeling on asset performance, customer trends, etc.
Lack of real-time insights, unscheduled downtimes, and static maintenance routines all combine to create network hardware infrastructures that perform well below their optimum capability.
Legacy network security applications that do not provide the AI-enabled, Zero-Trust capabilities required to protect networks, devices, and customers.
Several technology and operational limitations need to be overcome for telecom operators, MVNOs, OEMs, and CSPs to create new streams of revenue that capitalize on advancements in AI, ML, 5g, and IoT.
Cloud processing of petabytes of data creates massive costs for telecom operators, impacts bottom lines, and restricts ability to offer new pricing levels.
MicroAI’s core technologies, AtomML™ and AtomML+™ represent the future of embedded and edge AI capabilities for the telecom segment. By moving intelligence enablement and training much closer to broadband and wireless networks, their assets, and their users, the MicroAI products and solutions provide a more transparent, and more interconnected, approach to managing today’s complex telecom infrastructures.
Embedded and Edge-based intelligence that collects, synthesizes, and streams network, machine, and device data from multiple endpoints providing real-time transparency and insights.
Machine learning that is designed specifically to run on network equipment and devices with limited memory and compute capacity.
The unique ability to provide real-time performance observability into an individual machine or into large groups of devices within the telecom network.
Multivariate analysis is performed across large volumes of input channels to generate predictive analytics related to network performance, hardware maintenance, customer usage trends, and more.
More data processed at the endpoint or the edge, vs in the cloud, reduces overall data handling cost by 70 to 80% and eliminates problems associated with data latency.
Self-learning algorithms provide the real-time intelligence needed to protect networks, devices, and customers from today’s sophisticated cyber threats.
Telecom operators are bombarded by malicious actors looking to defraud customers and operators. Globally, losses related to fraud can exceed billions of dollars annually. MicroAI provides AI-enabled predictive security that provides protection against…
MicroAI’s Enterprise AI for Telecom will provide telecom companies with the means to develop differentiated offerings
while also improving operational efficiencies and improving customer stickiness.
Transformational benefits will include:
Intelligent network optimization that gives operators the ability to optimize network efficiencies, prevent failure, improve device to network visibility, and predict future bandwidth requirements.
The ability to innovate new offerings based on predicitve insights into future trends in IoT development, cyber threats, fraud types, and customer demands.
Self-learning security protocols embedded at the network and asset levels that deliver state-of-the-art security to operators, OEMs, and customers.
Quality of Service (QoS) metrics generated by AI-enabled analytics power the fine tuning of the customer experience and improves brand loyalty.
The ability to analyize and visualize data from networks, devices, customer interactions, and service trends will accelerate the development of new products and services and improve customer retention.
Signficant reductions in cost associated with network and device data transmission will have a major impact on OPEX and bottom-line results.
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