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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

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...