Infovista introduced Ativa™ Optimize to deliver operators geospatial analytics, monitoring, troubleshooting and optimization across all their radio vendors and technologies – from 2G to 5G standalone – in a single pane of glass.
Part of the cloud-native Ativa suite of applications and solutions for automated assurance and operations, Ativa Optimize enables operators to streamline previously laborious processes through automated RAN diagnostics and recommendations, reducing swivel-chair operations and ensuring best performance and user experience (UX).
Built on the unified NLA Cloud Platform™ and leveraging best-in-class geolocation accuracy and actionable reporting, Ativa Optimize helps MNOs easily understand and visualize radio network performance and correlate it with user experience using geolocated subscriber call trace data. This enables operators to identify areas with the most customer impact and prioritize network operations accordingly, and monitor high-value customers and zoom down to individual subscribers for faster investigation and troubleshooting.
Ativa Optimize brings CAPEX and OPEX reductions by offering a futureproof unified cloud-based solution to manage all RAN networks, improving operational efficiency through advanced analytics and automation, and significantly reducing the number and cost of drive tests through analysis of subscriber call and device behavior. It also leverages its geospatial insights across the organization, helping planning, customer care, and marketing teams take well-informed business-driven decisions and improve the bottom-line.
“In programable networks, radio optimization is no longer a dedicated silo process but a continuous one, tightly connected to Service Assurance,” said Renata Da Silva, VP Product, Service Assurance at Infovista. “Having visibility of the right data at the right time to identify, troubleshoot, optimize and correct issues automatically is vital. Connecting customers’ strong demand for wireless connectivity to RAN resource constraints is mandatory for operators’ engineering teams, especially when more numerous and more complex radio technologies must be managed.”
Operators can correlate data from RAN to core by integrating Ativa Optimize with Ativa Experience and Ativa App, for end-to-end customer experience, service and application intelligence. This 360° visibility of network, service performance and user experience allow more systematic problem assessment, validation and prioritization. The Ativa suite of applications and solutions, which can be deployed independently or in combination, correlate experience, service quality and resource performance across domains to deliver end-to-end automated assurance through a single pane of glass.
Ativa Optimize enables 360º Assurance for VoLTE and VoNR, a comprehensive, end-to-end solution to ensure better customer experience when using voice services in dynamic networks. The solution reduces the complexity of assuring mobile voice services delivered through 4G and 5G networks using VoLTE and VoNR, while reducing operational complexity and costs through automation.
Ativa is powered by the NLA Cloud Platform, Infovista’s common cloud-native platform for network planning, testing, and assurance solutions that enables CSPs to reduce network operation costs and time-to-market of new services. The NLA Cloud Platform provides common telco-specific cloud-native automation, analytics and data correlation engines to power Infovista solutions across the full network lifecycle, including Infovista’s Planet AI-driven RF network planning, TEMS™ network testing and the full Ativa Suite for automated assurance and operation. This translates into efficiency and productivity gains by reducing the footprint of previously siloed systems, streamlining operability and reducing management overhead through a unified cloud-native platform common to all Infovista solutions.
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 ...