Alarm Overload
More than 90% of raw alarms can be noise or duplicates, increasing triage effort and making critical incidents harder to isolate.
Autonomous Network Operations
From Reactive Operations to Autonomous, Self-Healing Networks
We combine AI-driven NOC operations, predictive network intelligence and automated resolution to detect, diagnose and resolve network issues before they impact subscribers.
Industry Challenges
Network Complexity Is Outpacing Traditional NOC Operations
As network complexity increases, traditional NOC models are becoming increasingly difficult to scale. High alarm volumes, fragmented monitoring and manual investigation keep operations reactive, while growing 5G complexity increases the need for specialised network expertise.
Slow Root-Cause Analysis
Faults can span RAN, Core, Transport and IP, making the underlying cause difficult and time-consuming to identify.
Reactive Operations
Traditional monitoring can surface problems only after services are affected, increasing risk to SLA performance, revenue and customer experience.
Fragmented Visibility
Separate monitoring environments create blind spots across network domains and make event correlation more difficult.
Specialist Resource Constraints
Growing 5G complexity increases dependence on experienced network engineers who remain difficult and expensive to scale.
Solution Overview
Transform Network Operations with AI-Driven Autonomy
We bring together AI-driven operations, DPI intelligence, predictive analytics, NWDAF, active testing and service assurance to shift operations from reactive response towards predictive, preventive and increasingly autonomous action.
01
Cut Through Alarm Noise
We correlate large volumes of network alarms into prioritised incidents, helping NOC teams focus on issues that require action.
02
Identify Root Causes Faster
We connect events across network domains to identify probable root causes and provide the operational context needed for faster resolution.
03
Predict Issues Earlier
We combine DPI intelligence, network analytics and machine learning to identify anomalies, capacity risks and emerging faults before they escalate.
04
Automate Resolution with Control
We use governed agentic runbooks to execute approved corrective actions while maintaining defined operational controls, auditability and reversibility.
05
Assure Services Continuously
We combine active testing with network and service intelligence to continuously validate performance across voice, data and signalling.
06
Advance Network Autonomy
We enable operators to progressively increase automation across network operations while retaining visibility, governance and operational control.
Solution Capabilities
Intelligence That Moves from Detection to Action
We connect network data, AI-driven analysis and controlled automation across the operational environment, enabling NOC teams to move from detecting individual events to understanding, predicting and resolving network conditions more intelligently.
OSS/BSS & Network Integration
We integrate with existing OSS/BSS and network environments through open APIs, bringing together alarms, traffic data, network analytics and service-assurance signals across multi-vendor environments.
AI-Driven Incident Intelligence
We use machine learning to correlate thousands of raw alarms into prioritised incidents, reducing alarm noise by more than 90%. AI-assisted root-cause analysis connects related network events to identify probable causes and provide supporting evidence for faster resolution.
Predictive Network Intelligence
We combine DPI intelligence and machine learning to detect traffic anomalies, protocol deviations and capacity trends before thresholds are breached, enabling teams to act before emerging conditions develop into service-impacting events.
Governed Autonomous Remediation
We use pre-built and configurable agentic runbooks to execute remediation within defined autonomy levels. Actions remain auditable, reversible and governed by operator-defined controls, allowing automation to expand without compromising operational oversight.
5G and Multi-Domain Intelligence
We combine NWDAF analytics with visibility across network domains, technologies and vendors. Real-time intelligence into slice performance, UE mobility, QoS and network KPIs provides a connected view of network and service conditions across 5G environments.
Continuous Service Assurance
We integrate AssureTest for active validation across voice, data and signalling paths, enabling service performance to be assessed independently of subscriber complaints. NOC teams can also query network health, incident history and runbook status through a natural-language operational interface.
Business Impact
Measurable Gains Across Network Operations
70% Reduction in Mean Time to Repair
We accelerate root-cause identification and resolution, reducing incident-resolution time from hours to minutes and limiting service impact.
90%+ Reduction in Alarm Noise
We transform thousands of raw alarms into a manageable set of prioritised incidents, enabling NOC teams to focus on genuine network problems.
60% Reduction in NOC Workload
We automate routine incident handling, helping operators manage increasing network complexity without proportional increases in NOC resources.
Detect Network Faults 30–60 Minutes Earlier
We use predictive intelligence to identify emerging issues earlier, creating more time to intervene before services are affected.
Faster 5G Rollout Assurance
We combine NWDAF intelligence and multi-domain visibility to help new 5G sites, slices and services meet performance targets from launch.
Measurable Progress Towards Autonomy
We align automation with the TM Forum autonomy framework, helping operators advance through defined autonomy levels with clear governance and measurable KPIs.
Use Cases
AI-Driven NOC Transformation
Autonomous Operations Across Live Network Environments
A Tier-1 operator with 50M+ subscribers needed to manage more than 2M alarms every day across its RAN…
A Tier-1 operator with 50M+ subscribers needed to manage more than 2M alarms every day across its RAN and Core NOC, making manual triage and incident resolution increasingly difficult to scale. TVS-AIOps correlates this alarm volume into approximately 200 actionable incidents, while agentic runbooks automatically resolve 40% of incidents. As a result, MTTR falls from 4 hours to 45 minutes, allowing NOC teams to spend less time responding to alarms and more time on network optimisation.
Predictive RAN Capacity Management
An operator needed to identify emerging cell-site congestion before it began affecting subscriber experience. We use DPI analytics…
An operator needed to identify emerging cell-site congestion before it began affecting subscriber experience. We use DPI analytics and machine-learning models to analyse traffic patterns and relevant contextual data, forecasting congestion up to 48 hours in advance. Automated load-balancing recommendations and actions help the operator prevent 30% of congestion-related service degradations before they occur.
5G Slice Assurance with NWDAF
An operator providing enterprise 5G slices needed to continuously maintain committed service levels as network conditions changed. We…
An operator providing enterprise 5G slices needed to continuously maintain committed service levels as network conditions changed. We use NWDAF-integrated monitoring to track slice performance and identify emerging QoS degradation. When performance begins to drift, automated corrective actions adjust resource allocation before service quality is affected, helping maintain the experience committed to enterprise customers.
Cross-Domain Fault Isolation
A regional service outage required teams to determine the underlying fault across multiple network domains, a process that…
A regional service outage required teams to determine the underlying fault across multiple network domains, a process that would traditionally involve a lengthy cross-functional war room. AI-driven root-cause analysis correlates network events across domains and identifies the underlying fibre cut within 3 minutes, compared with a 2-hour war-room process. Automated notifications can then inform affected enterprise customers before they report the issue.