Our client, a leading telecom operator with over 25 million subscribers and 1,500 enterprise accounts, delivers mobile, broadband, and managed network services across multiple regions. Enterprise contracts especially with global corporations and government bodies required strict SLA compliance covering network uptime, service restoration, and ticket resolution. With multi-million-dollar contracts on the line, SLA breaches not only resulted in penalties running into millions annually but also created reputational damage and customer churn risk.

The Challenge: Missed SLAs, Penalties, and Reputation Risk

The telecom provider faced several operational bottlenecks and financial risks due to fragmented SLA management and reactive processes. Key challenges included:

• Reactive SLA Management: Existing monitoring tools flagged breaches only after they occurred, leaving no room for proactive intervention.
• Fragmented Visibility: SLA metrics were scattered across OSS, BSS, CRM, and ITSM systems, with no unified view.
• Escalation Overload: Customer complaints and escalations consumed service managers’ time instead of allowing them to focus on improvement.
• High Penalty Costs: Missed SLA obligations cost the provider over $5 million annually in penalties.
• Churn Risk: Key enterprise clients expressed dissatisfaction, threatening contract renewals. 

The client realized it needed a predictive, AI-driven SLA monitoring framework to shift from reactive firefighting to proactive assurance.

The Solution: Predictive SLA Monitoring with AI + Automation

Amantra implemented a comprehensive SLA intelligence platform, combining AI forecasting, RPA-driven data integration, and real-time analytics.

1. Unified SLA Data Collection
   • RPA bots pulled SLA data from multiple systems incident management, ticketing tools, network monitoring, and CRM.
   • Eliminated manual collation, ensuring near real-time updates.
2. AI-Driven SLA Risk Forecasting
   • Machine learning analyzed historical SLA breaches and ticket resolution patterns.
   • AI models predicted which tickets or services were at risk of breaching SLAs days or hours in advance.
3. Proactive Alerts & Automated Escalations
   • High-risk tickets triggered alerts to service teams, with escalation workflows automatically routed to managers.
   • RPA bots reprioritized workloads, ensuring high-impact tickets were addressed first.
4. Executive SLA Dashboard
   • Leadership gained a single view of SLA compliance across regions, accounts, and service categories.
   • Drill-down reports highlighted recurring problem areas and underperforming processes. 

The Results: SLA Compliance at Scale

• 40% reduction in SLA breaches within the first 9 months.
• SLA-related penalties decreased by 40%, saving the provider $3.5 million annually.
• Proactive resolution rate improved by 68%, cutting escalations drastically.
• 25% improvement in enterprise customer satisfaction scores (CSAT).
• Renewals and contract extensions improved as SLA reliability became a competitive differentiator. 

Bottom Line: With Amantra 's predictive SLA monitoring, the telco moved from reactive SLA firefighting to a data-driven, proactive approach that protected revenue, strengthened client trust, and ensured operational excellence.