24/7 Operations with Limited Staff
Lean teams cannot maintain traditional round-the-clock NOC staffing, increasing the risk that off-hours incidents go undetected until subscribers are affected.
Advanced Network Operations with Leaner Teams
We combine AI-driven operations, automated incident response, continuous service testing and managed AI capabilities to help lean MVNO and IoT teams operate with greater visibility, control and scale.
Industry Challenges
Always-On Services Put Lean Operations Teams Under Pressure
MVNOs and IoT providers need to maintain reliable services around the clock, often without the resources of a traditional NOC. Limited visibility, manual incident handling and gaps in service assurance make it harder for small teams to stay ahead of operational issues as the business grows.
Dependency on MNO Visibility
MVNOs depend on performance data from their MNO partners, which may not provide enough detail or immediacy for effective service management.
Manual Incident Handling
Every alarm that requires manual assessment and response adds pressure to already limited operations resources.
Service Validation Gaps
Without continuous service testing, degradation may only become visible after subscribers begin reporting problems.
Limited In-House AI Expertise
Building and maintaining custom operational AI models requires specialist skills that lean operators may not have internally.
Solution Overview
Give Lean Teams Greater Operational Reach
We connect AI-driven operations, continuous service validation and managed AI capabilities to reduce the manual effort required to monitor and manage services. This gives lean teams broader operational coverage while keeping human expertise focused on incidents and decisions that genuinely require it.
01
Extend Operations Around the Clock
AI-driven monitoring and incident handling provide continuous operational coverage without requiring equivalent round-the-clock staffing.
02
Find and Resolve Issues Earlier
Automated incident intelligence and continuous service testing help identify degradation, understand its likely cause and initiate appropriate action before issues escalate.
03
Scale Capability Without Scaling Teams
Automation absorbs more of the monitoring and routine operational workload as subscriber, device and service volumes increase.
04
Build Operational Intelligence Without a Dedicated AI Team
AI-as-a-Service provides a managed route to develop custom analytics for areas such as churn, usage and capacity planning.
Solution Capabilities
Automate More of the Operational Workload
AI-driven monitoring, service assurance and automated response work together to give lean teams greater control over day-to-day operations.
TVS-AIOps - AI-Driven Operations
TVS-AIOps automates alarm processing, incident prioritisation and routine response around the clock, reducing the need for dedicated NOC resources.
Automated Incident Response
AI handles first-level incident assessment, prioritises events and resolves routine issues where appropriate, escalating incidents that require human judgement.
Automated Operational Workflows
Predefined workflows execute common actions such as service restarts, threshold adjustments and partner escalation within defined policy guardrails.
AssureTest - Continuous Service Testing
Automated testing continuously validates critical voice, data and signalling services, helping identify degradation before it becomes a wider subscriber issue.
MNO & Service Performance Visibility
Performance data from MNO partners is correlated with the operator's own infrastructure monitoring to create a broader view of service health and incidents.
AI-as-a-Service for Custom Analytics
Managed AI capabilities support custom models for churn prediction, usage forecasting and capacity planning without requiring an in-house data-science team.
Intelligent Alerting & Escalation
Alerts are prioritised according to factors such as severity, impact and team availability, helping direct the right issues to the right people at the right time.
Service Assurance Visibility
A configurable view of service health, active incidents, SLA performance and operational trends gives lean teams the information needed to manage services without unnecessary monitoring complexity.
Business Impact
Scale Operations Without Scaling Complexity
24/7 Operations Without 24/7 Staffing
AI-driven incident handling provides continuous operational coverage, helping address off-hours events without requiring engineers to respond manually to every alert.
80% Reduction in Manual Incident Handling
Automated incident assessment and operational workflows handle routine events end to end, freeing teams to focus on optimisation, service quality and growth.
Earlier Issue Detection
Continuous service testing can identify degradation 30+ minutes before subscribers notice, helping shift operations from reactive troubleshooting towards proactive assurance.
Broader End-to-End Service Visibility
Correlating the operator's own monitoring with MNO partner data provides greater visibility across the service chain than relying on partner dashboards alone.
Custom AI Without Dedicated Data Scientists
AI-as-a-Service provides a managed route to build operational models for churn prediction, usage forecasting and other operator-specific requirements.
More Scalable Operations
As service volumes grow, automation absorbs more of the monitoring and incident workload, reducing the need for operations headcount to increase at the same rate.
Use Cases
Lean Operations in Action
Night-Time Incident Response
A digital MVNO with 500K subscribers and a four-person operations team deployed TVS-AIOps for 24/7 coverage. At 2…
A digital MVNO with 500K subscribers and a four-person operations team deployed TVS-AIOps for 24/7 coverage. At 2 am, the system detected signalling degradation, connected it to an MNO maintenance event, verified subscriber impact through active testing and sent a targeted notification to affected subscribers without human intervention. The on-call engineer reviewed the incident summary the following morning.
Continuous Service Quality Validation
AssureTest ran automated voice and data tests every 15 minutes across all service regions. When testing identified a…
AssureTest ran automated voice and data tests every 15 minutes across all service regions. When testing identified a 50 ms latency increase in one region, the system linked it to a backbone router issue at the MNO and opened a ticket with the MNO's NOC before subscribers reported the change.
Churn Prediction for Retention
Using AI-as-a-Service, an operator built a churn prediction model using subscriber behaviour, complaint history and service-quality data. The…
Using AI-as-a-Service, an operator built a churn prediction model using subscriber behaviour, complaint history and service-quality data. The model identified 2,000 at-risk subscribers each month, triggering automated retention offers that reduced monthly churn by 15%.
Scale-Up Without Staff-Up
An IoT provider grew from 200K to 2M connected devices over 18 months. AI-driven operations absorbed the resulting…
An IoT provider grew from 200K to 2M connected devices over 18 months. AI-driven operations absorbed the resulting increase in monitoring events, alarm volumes and incident complexity while the operations team added only one additional engineer.