A telecom operator's call center handled 4M contacts annually with 2,800 agents. Average handle time was 14 minutes, with 35% of calls being simple billing inquiries that agents could resolve in 2 minutes but required 12 minutes of system navigation.

CSAT scores had dropped to 3.0 out of 5. Technical troubleshooting calls averaged 22 minutes because agents lacked diagnostic tools and followed static scripts. Customer context was lost between channels, forcing customers to repeat information.

The operator needed to automate routine queries, enhance technical support, and provide seamless cross-channel experiences.

The Solution

Agents that classify intent, resolve queries, diagnose issues, and escalate with context

Vijan.AI deployed 4 agents. The Intent Classifier categorizes requests across 60+ intent categories (billing, technical, account, sales) with 96% accuracy. The Resolver agent handles routine queries (balance checks, plan changes, payment processing, usage inquiries) via BSS APIs. The Diagnostic agent troubleshoots network and device problems using real-time network data, device diagnostics, and knowledge base. The Escalation agent transfers complex cases to human agents with full interaction history, diagnosis results, and recommended resolution.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Inquiry

Subscriber Inquiry Agent

Subscriber Inquiry Triage & Resolution

Routes customer inquiries by type, autonomously resolving billing questions, technical issues, and plan changes while escalating complex cases to specialized agents.

Input

Customer inquiries, CRM records, billing history, technical logs

Output

Inquiry responses, case closures, escalation routing, satisfaction scores

Calls CRM API to retrieve subscriber account details and interaction history
Calls case database to search knowledge base for common issue resolutions
Autonomous decision: auto-resolve FAQ, route to Restoration, Billing, or Churn agents
Routes unresolved cases to appropriate specialist with full context

Step 2 · Restoration

Service Restoration Agent

Technical Issue Diagnosis & Restoration

Diagnoses service outages, device issues, and network problems, autonomously resetting equipment, dispatching technicians, or provisioning device swaps to restore service.

Input

Service tickets, network alarms, device diagnostics, crew availability

Output

Restoration plans, remote resets, technician dispatches, service confirmations

Calls network database to check tower status, coverage maps, and outage alerts
Calls dispatch system to assign field technicians for on-site repairs
Autonomous decision: remote reset, schedule truck roll, or provision replacement device
Routes unresolved or billing-related cases to Billing agent

Step 3 · Billing

Bill Dispute Agent

Billing Dispute Investigation & Resolution

Audits disputed charges against usage data and pricing plans, autonomously issuing credits, adjusting plans, or escalating fraud cases to collections.

Input

Dispute tickets, billing records, usage logs, pricing catalogs

Output

Credit approvals, plan adjustments, dispute resolutions, fraud escalations

Calls billing database to retrieve itemized charges and usage details
Calls dispute API to validate charges against contract terms and usage evidence
Autonomous decision: issue credit, adjust plan, or escalate to fraud or legal team
Routes high-value or at-risk accounts to Churn agent for retention offers

Step 4 · Churn

Churn Intervention Agent

Post-Issue Retention & Satisfaction Recovery

Identifies dissatisfied customers post-resolution and autonomously offers loyalty rewards, plan upgrades, or credits to prevent churn and improve CSAT.

Input

Case outcomes, satisfaction scores, churn risk, retention budgets

Output

Retention offers, loyalty rewards, satisfaction surveys, churn prevention reports

Calls churn prediction model to score dissatisfaction risk from case history
Calls retention API to generate personalized offers or bill credits
Autonomous decision: send apology credit, offer upgrade, or schedule follow-up call
Routes retention outcomes to CRM for account notes and marketing segmentation

Results

Measurable impact within 90 days of deployment

75%

Auto-Resolution

75% of customer contacts resolved without human agents. Remaining 25% transferred with full context.

4.4/5

CSAT Score

Customer satisfaction improved from 3.0 to 4.4. First-contact resolution rate reached 88%.

$42M

Annual Savings

Call center staff optimized from 2,800 to 1,200 agents handling only complex cases. AHT reduced 60%.

< 30sec

Response Time

Average first response time reduced from 6 minutes to under 30 seconds across all channels.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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