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Infovista Unveils NLA Cloud Platform

Infovista unveiled the NLA Cloud Platform™ that unifies its network planning, testing, and automated assurance and operations products and solutions.

Integrating data, workflows, and analytics across the network lifecycle and breaking the limitation of traditional siloed-solution approaches, the NLA Cloud Platform brings greater use case innovation, agility, and interoperability for CSPs’ throughout and across their next-generation fixed and mobile networks.

The NLA Cloud Platform provides common telco-specific functions such as automation, analytics, and data correlation engines to power Infovista solutions across the entire network lifecycle. The platform extension builds on the successful deployment of the platform powering the Ativa™ Suite of applications for automated assurance and operation to now also include Planet AI-driven RF network planning and TEMS™ network testing solutions. This translates into efficiency and productivity gains by reducing the footprint of previously siloed architectures, streamlining operability, and reducing management overhead through a unified cloud-native platform common to all Infovista solutions, which can be deployed independently or in combination on the NLA Cloud Platform.

The cloud-native architecture delivers a high level of openness thanks to a standard and extensible suite of adaptors and parsers for data collection from network nodes or other third-party systems. This enables the integration of multiple data sources – such as probe data, call traces, OSS, drive test and third-party data sources such as crowdsourced data, EMS data and post-processed data from other assurance, planning and testing solutions – to be consolidated and correlated to provide in-depth information and actionable insights in a single pane of glass.

“Regardless of where an operator is in its journey to becoming cloud-native or enabling 5G-SA, they want to know that they can easily and cost-effectively automate processes and extend the benefits of shared data and analytics across their network as and when they’re ready,” said Franco Messori, Chief Product Strategy & Transformation Officer, Infovista. “To deliver CSPs multi-segment end-to-end visibility from infrastructure to subscriber, and the insight and control they need across their networks, services, and experiences, the framework must be cloud-native by design, open, and flexible. Building on 30+ years of telco experience unrivalled by other cloud platforms, we have completely redesigned the solution architecture. Our new unified cloud platform represents more than a significant investment in modernizing how traditionally siloed applications are deployed; it unlocks benefits for CSP customers which are only possible with network lifecycle automation.”

The NLA Cloud Platform is fully containerized and provides common building blocks for all Infovista’s solutions such as a common web portal and engines for correlation, analytics, alerting and ML/AI automation of everyday workflows. This results in reduced footprint and resource utilization for improved TCO and energy consumption and a simplification in deployments and configurations. The platform reduces complexity further by offering a single monitoring and maintenance portal for software updates across the network lifecycle, single sign-on user access for applications and use cases, and a standard user interface across the different solutions.

The initial Network Lifecycle Automation use cases powered by Infovista’s network planning, testing and assurance solutions hosted on the NLA Cloud Platform include, but are not limited to:

- Smart CAPEX – for rapid, AI/ML-enabled ROI-driven optimization of roll-outs and network densification/expansion. The solution uses advanced Digital Twin and ML-driven scenario analysis, combining network performance, business KPIs such as revenue and churn, and network TCO predictions to accurately optimize network planning for optimized ROI

- Active Testing & Assurance – to correlate drive-test and service assurance data for new actionable insights, comprehensive troubleshooting and on-demand active testing through Precision Drive Testing™, Infovista’s data-driven ML/AI-based approach for targeted and automated network drive tests

- 360º Assurance – a new family of use cases providing 360° Assurance of SLA-backed 5G services based on 5G slicing, VoLTE/VoNR and fixed voice and broadband services, as well as end-to-end monitoring and customer and device analytics

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Infovista Unveils NLA Cloud Platform

Infovista unveiled the NLA Cloud Platform™ that unifies its network planning, testing, and automated assurance and operations products and solutions.

Integrating data, workflows, and analytics across the network lifecycle and breaking the limitation of traditional siloed-solution approaches, the NLA Cloud Platform brings greater use case innovation, agility, and interoperability for CSPs’ throughout and across their next-generation fixed and mobile networks.

The NLA Cloud Platform provides common telco-specific functions such as automation, analytics, and data correlation engines to power Infovista solutions across the entire network lifecycle. The platform extension builds on the successful deployment of the platform powering the Ativa™ Suite of applications for automated assurance and operation to now also include Planet AI-driven RF network planning and TEMS™ network testing solutions. This translates into efficiency and productivity gains by reducing the footprint of previously siloed architectures, streamlining operability, and reducing management overhead through a unified cloud-native platform common to all Infovista solutions, which can be deployed independently or in combination on the NLA Cloud Platform.

The cloud-native architecture delivers a high level of openness thanks to a standard and extensible suite of adaptors and parsers for data collection from network nodes or other third-party systems. This enables the integration of multiple data sources – such as probe data, call traces, OSS, drive test and third-party data sources such as crowdsourced data, EMS data and post-processed data from other assurance, planning and testing solutions – to be consolidated and correlated to provide in-depth information and actionable insights in a single pane of glass.

“Regardless of where an operator is in its journey to becoming cloud-native or enabling 5G-SA, they want to know that they can easily and cost-effectively automate processes and extend the benefits of shared data and analytics across their network as and when they’re ready,” said Franco Messori, Chief Product Strategy & Transformation Officer, Infovista. “To deliver CSPs multi-segment end-to-end visibility from infrastructure to subscriber, and the insight and control they need across their networks, services, and experiences, the framework must be cloud-native by design, open, and flexible. Building on 30+ years of telco experience unrivalled by other cloud platforms, we have completely redesigned the solution architecture. Our new unified cloud platform represents more than a significant investment in modernizing how traditionally siloed applications are deployed; it unlocks benefits for CSP customers which are only possible with network lifecycle automation.”

The NLA Cloud Platform is fully containerized and provides common building blocks for all Infovista’s solutions such as a common web portal and engines for correlation, analytics, alerting and ML/AI automation of everyday workflows. This results in reduced footprint and resource utilization for improved TCO and energy consumption and a simplification in deployments and configurations. The platform reduces complexity further by offering a single monitoring and maintenance portal for software updates across the network lifecycle, single sign-on user access for applications and use cases, and a standard user interface across the different solutions.

The initial Network Lifecycle Automation use cases powered by Infovista’s network planning, testing and assurance solutions hosted on the NLA Cloud Platform include, but are not limited to:

- Smart CAPEX – for rapid, AI/ML-enabled ROI-driven optimization of roll-outs and network densification/expansion. The solution uses advanced Digital Twin and ML-driven scenario analysis, combining network performance, business KPIs such as revenue and churn, and network TCO predictions to accurately optimize network planning for optimized ROI

- Active Testing & Assurance – to correlate drive-test and service assurance data for new actionable insights, comprehensive troubleshooting and on-demand active testing through Precision Drive Testing™, Infovista’s data-driven ML/AI-based approach for targeted and automated network drive tests

- 360º Assurance – a new family of use cases providing 360° Assurance of SLA-backed 5G services based on 5G slicing, VoLTE/VoNR and fixed voice and broadband services, as well as end-to-end monitoring and customer and device analytics

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In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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