Net AI
Mark Chapman has a diverse work experience in various industries. Mark is currently serving as the Chief Executive Officer at GraphEnergy Corp, where they lead the development of graphene-based battery materials for improved performance. Mark is also a Co-Founder of Net AI, a real-time network analytics company, focusing on providing AI-based solutions for effective management of mobile networks.
Mark has extensive experience in the semiconductor industry, serving as a Board Member at Carbon Technology Inc, a company specializing in carbon nanotubes and carbon electronics research. Mark was also the President and General Manager at Ascom Inc, where they successfully rebuilt the wireless test and measurement segment.
Additionally, Mark has worked in the wireless industry, serving as a Director at EDX Wireless, a technology leader in wireless system planning solutions. Mark also provided strategic consulting services as the Principal of Chapman Associates, specializing in business development and strategic planning for wireless semiconductors and systems.
Mark has a background in aviation as well, as the Founder and Partner of 5g Aviation, an aircraft sales and training company. Mark has also served as a Board Member at Quatech and worked as the Vice President of Business Development at Wispry.
Throughout their career, Mark has demonstrated expertise in strategic planning, business development, and leadership roles across various industries.
Mark Chapman attended Fraserburgh Academy from 1975 to 1979. Mark then enrolled at Robert Gordon University from 1979 to 1982, where they obtained a BSC (Hons) Ist Class degree in Electronic and Electrical Engineering. There is no information provided regarding their enrollment or degree at The University of Edinburgh.
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Net AI
Net AI had developed a cloud-native platform that uses AI to provide real time analytics, which can drive the optimization of virtualized mobile networks. The volume of mobile data traffic is exploding and 5G will have to fulfill a growing variety of performance requirements, ranging from extreme mobile broadband to low-latency automotive IoT. Toaccommodate such demands, enhanced flexibility in managing the infrastructure is needed. Network slicing allows operators to customize resources on a per-service basis, by virtually partitioning the physical infrastructure, thereby enabling new lucrative revenue streams. However, without deep intelligence into the traffic flowing over slices, and where it originates, it is impossible to effectively and efficiently monetize them. To address this need, our Microscope software uses AI to perform mobile traffic decomposition. Microscope identifies and quantifies the nature and source of individual streams (e.g. Netflix, Google cloud services, Facebook, etc; down to individual base station) from aggregate streams. This allows data to be collected in the cloud rather than from expensive location-based probes, and also works with encrypted data sources which are impossible to analyze with traditional approaches such as using Deep Packet Inspection (DPI). Our technology tackles the challenges of decomposition through deep learning, due to its effectiveness in operating on large-scale mobile traffic in real-time, as demonstrated by our own research. Microscope is fast, cheap, encryption agnostic, scalable, and compatible with current NFV and open RAN initiatives