Telecommunications
Southeast Asia
Telco: Agent-Ready Data Platform
A telco's data warehouse was built for analysts — not AI agents. Rotavision rebuilt their data architecture for the agent era: faster, cheaper, and with context that stopped hallucinations.
Challenge
Data platform built for humans, not agents
The telco was deploying AI agents across operations: network anomaly detection, customer service, fraud detection, and churn prediction. But the data platform became the bottleneck:
Scale mismatch
- Data warehouse designed for 50 analysts
- AI agents generating 100x query volume
- Query queues backing up, timeouts increasing
Context gaps
- Agents hallucinating due to incomplete data
- No semantic understanding of telco-specific terms
- Customer context fragmented across 7 systems
Latency issues
- Real-time network ops needed sub-second response
- Data warehouse returning results in minutes
- Agents making decisions on stale data
Cost explosion
- Data platform costs up 300% in 6 months
- No visibility into what was driving queries
- Agents querying redundantly
Approach
SARP and ETL-C implementation
Phase 1
Weeks 1-3
Assessment
- Analyzed agent query patterns
- Identified context gaps causing hallucination
- Mapped latency requirements by use case
- Assessed current architecture limitations
Phase 2
Weeks 4-6
Architecture Design
- Designed agent-ready data architecture (SARP)
- Created context model for telco domain (ETL-C)
- Specified semantic query layer
- Planned migration approach
Phase 3
Weeks 7-16
Implementation
- Deployed Context Engine for unified customer/network view
- Built semantic query API for agents
- Implemented agent-optimized caching layer
- Created real-time streaming layer for network ops
Phase 4
Weeks 17-20
Optimization
- Tuned caching based on actual patterns
- Optimized query routing
- Implemented cost attribution
- Trained platform team
Solution
Agent-ready data architecture
SARP implementation
| Layer | Function | Technology |
|---|---|---|
| Semantic Query API | Natural language to structured query | Custom + LLM |
| Agent Cache | High-frequency query results | Redis Cluster |
| Context Store | Embeddings, metadata, relationships | Pinecone + Neo4j |
| Streaming Layer | Real-time network data | Kafka + Flink |
| Data Lake | Historical analysis | BigQuery |
ETL-C for telco
- Unified customer context (CRM, billing, usage, support, network)
- Network topology context (relationships, dependencies)
- Temporal context (usage patterns, seasonal trends)
- Semantic enrichment (telco terminology, product catalog)
Agent-specific optimizations
- Pre-computed features for common agent queries
- Materialized views for real-time dashboards
- Query result caching with TTL by data type
- Rate limiting by agent priority
Results
Data platform for the agent era
| Metric | Before | After | Change |
|---|---|---|---|
| Agent query latency (p95) | 4.2s | 180ms | -96% |
| Query throughput | 500/min | 15,000/min | +2,900% |
| Agent hallucination rate | 18% | 4% | -78% |
| Data platform cost | $180K/mo | $95K/mo | -47% |
| Network ops response time | 45 min | 3 min | -93% |
Additional outcomes
- Network auto-remediation coverage expanded 5x
- Customer service AI accuracy up 23%
- Churn prediction precision improved 31%
- Platform team able to support 3x more agents
"We had a data warehouse built for humans and AI agents that needed something completely different. Rotavision redesigned our data architecture for the agent era — faster, cheaper, and with context that stopped our agents from making things up."
— Chief Data Officer
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