TUPL has contributed to beta testing of Radio Access Network Automation in SoftBank with TUPL’s Network Advisor


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BELLEVUE, Washington – September 10, 2018 – Tupl, a market leader in automation for the wireless industry, announced that they concluded a beta test to automate tuning of SoftBank’s Radio Access Network (RAN). In the beta test, Tupl developed an AI model to prepare SoftBank’s radio network for AI automation using their Network Advisor product and assessed the effectiveness of the model. Beta test results indicated that Tupl’s AI model was more than 90% accurate in a specific area of assessing network issues, and proved it is more effective and advanced in executing network automation tasks which SoftBank has been promoting. Tupl will provide continued support for the next phase trial across RAN for commercial use.

 

Petri Hautakangas, Tupl CEO, said: “After a very successful beta, we are now moving to the next phase of our collaboration with SoftBank, which will bring state of the art AI automation to their network. We’re confident that we’re the right partner to work closely with SoftBank to enable significant process efficiencies and quality improvements for their RAN system.”

 

Tupl’s Network Advisor solution provides automatic network troubleshooting using machine learning to conduct root cause analysis and resolve issues. It supports both supervised and unsupervised learning. For supervised learning, it is possible to do intuitive training and re-training to improve the AI model. Tupl Network Advisor is perfect for complex networks including multilayer & multivendor networks which is a typical environment of Telecom networks, and various use cases. Tupl Network Advisor won a Fierce Innovation Award in 2017.

 

Learn more about the Tupl’s Network Advisor solution at www.tupl.com.

 

About Tupl

Founded in 2014 by telecom, big data and AI veterans, Tupl is transforming customer access and experience in the telecom industry thorough improving operations with leading wireless operators across the US, Canada, Japan, Mexico, and Europe. Its AI Engine, TuplOS, utilizes machine learning and several other utilities to enable faster innovation cycles for network and customer care operations. Tupl is headquartered in the US in Bellevue, Washington with presence in Spain, Mexico and Japan, and is continuing its rapid global expansion in 2018. To learn more about Tupl and request a demo, visit www.tupl.com.

 

Contact

Wistar Kay
Wistar.kay@tupl.com
+1.206.605.6602