Telecommunications
Agent-Ready Data Platform for Telecom
-96%
Query Latency
-78%
Hallucination Rate
-47%
Platform Cost
Customer Profile
One of India’s largest telecommunications companies with 15 crore+ subscribers, operating 4G/5G networks across all 22 telecom circles.
Challenge
The telecom’s enterprise data warehouse was designed for 50 human analysts running scheduled reports. When AI agents were deployed across network operations, customer service, and churn prediction, the infrastructure buckled:
Scale Mismatch
- AI agents generated 100x the query volume of human analysts
- Original design assumed batch processing; agents needed real-time responses
- Peak loads during network incidents caused complete system failures
Context Gaps
- Incomplete data caused 18% hallucination rates in customer service AI
- Network topology data was stale, leading to incorrect troubleshooting recommendations
- Customer history fragmented across 6 different systems
Latency Problems
- Network operations required sub-second responses for auto-remediation
- Actual query times averaged 4.2 seconds at p95
- Customer service agents timing out, forcing human escalation
Cost Explosion
- Platform costs increased 300% in six months
- No visibility into which agents consumed most resources
- Linear scaling with no optimization strategy
Solution
Rotavision implemented a four-phase transformation over 20 weeks:
Phase 1: Assessment (Weeks 1-4)
- Analyzed agent query patterns and peak load characteristics
- Traced hallucination root causes to data freshness and completeness gaps
- Mapped latency bottlenecks across the data pipeline
Phase 2: Architecture (Weeks 5-8)
- Designed agent-optimized data layer with semantic query understanding
- Created real-time streaming pipeline for network telemetry
- Built unified customer context from 6 legacy systems
Phase 3: Implementation (Weeks 9-16)
- Deployed Sankalp for sovereign data processing
- Integrated Guardian for hallucination monitoring
- Built agent-specific caching and query optimization
- Created streaming layer for sub-second network data
Phase 4: Optimization (Weeks 17-20)
- Fine-tuned caching strategies based on query patterns
- Trained platform team on agent workload management
- Established cost allocation by agent and use case
Technical Architecture
┌─────────────────────────────────────────────────────────┐
│ AI Agents Layer │
│ (Network Ops / Customer Service / Churn / Fraud) │
├─────────────────────────────────────────────────────────┤
│ Semantic Query API │
│ (Natural language to optimized queries) │
├─────────────────────────────────────────────────────────┤
│ Agent Cache Layer │ Real-time Stream │
│ (Query-pattern optimized) │ (Network telemetry) │
├─────────────────────────────────────────────────────────┤
│ Unified Context Engine │
│ (Customer 360 / Network Topology / CDR) │
├─────────────────────────────────────────────────────────┤
│ Source Systems │
│ (CRM / Billing / OSS / BSS / Network / CDR) │
└─────────────────────────────────────────────────────────┘
Results
| Metric | Before | After | Change |
|---|---|---|---|
| Query Latency (p95) | 4.2s | 180ms | -96% |
| Query Throughput | 500/min | 15,000/min | +2,900% |
| Hallucination Rate | 18% | 4% | -78% |
| Platform Cost | ₹1.5Cr/mo | ₹80L/mo | -47% |
| Network Response Time | 45 min | 3 min | -93% |
Business Impact
- Network auto-remediation coverage expanded 5x with reliable real-time data
- Customer service AI accuracy improved 23% with complete context
- Churn prediction precision improved 31% with unified customer view
- Platform team capacity increased to support 3x more agent use cases
- TRAI compliance improved with accurate network performance reporting
Operational Improvements
| Use Case | Before | After |
|---|---|---|
| Network Incident Detection | 45 min | 3 min |
| Customer Query Resolution | 4 min | 45 sec |
| Churn Risk Identification | Daily batch | Real-time |
| Fraud Detection | 6 hours | 15 min |
What’s Next
The telecom is expanding the platform to:
- 5G network slicing optimization
- Predictive maintenance for tower infrastructure
- Vernacular voice bot for customer service
Rotavision is powered by Rotascale’s globally-proven AI trust platform.
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