Voipfuture, a provider of a unique carrier-grade, dual-visibility voice service quality monitoring and analytics solution, has announced that it has been accepted into the Amazon Web Services (AWS) Independent Software Vendor (ISV) Accelerate Program, a co-sell program for AWS Partners who provide software solutions that run on or work with AWS. The program helps partners drive new business and accelerate sales cycles by connecting the participating ISVs with AWS.
Voipfuture’s premium voice service monitoring and analytics platform provides a unique technology for assessing, aggregating, analyzing, and visualizing voice quality information, for better data-based insights. It analyzes all call-related traffic in the cloud, automatically checks for impairments, isolates root causes and aggregates the data to create accurate statistics on relevant business entities. This enables Communication Service Providers (CSPs) to maintain full control over voice service-related customer experiences while benefiting from the flexibility and elasticity of AWS.
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Voipfuture Chief Executive Officer Jan Bastian said: “We have big plans for 2022 and beyond, supporting the telecommunication industry’s rapid move to the public cloud. Joining AWS’ program is a natural continuation of our work with AWS that begun last year when we delivered compelling results in a joint Voipfuture Qrystal-Amazon Lookout for Metrics trial.”
Through the AWS ISV Accelerate Program, Voipfuture will receive focused co-selling support from AWS and access to further sales enablement resources. The program was created to provide customers with better outcomes and assures mutual commitment from AWS and its Partners.
Voipfuture’s Qrystal Cloud is the only voice monitoring solution to deliver reliable and actionable user experience data using timeslicing, according to ETSI TR 103 639. This provides high temporal detail and the ability to aggregate the data for cells, interconnections, and complete services. Qrystal speeds up troubleshooting through behavioral analysis of users and automatic root cause indicators that reduce the time-to-fix by up to 80%. It is faster and easier to test, set up, and operate in the cloud than in conventional on-premise solutions. It is applicable to all phases of a CSP’s cloud journey, and its accurate service performance data lays the foundation for artificial intelligence (AI) / machine learning (ML)-based service automation in the cloud.
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