Business ConsultingCustomer Churn Prediction
Machine learning on call records, complaints and network logs to forecast churn.
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Enterprise AI for telecom operatorsLess energy. Fewer outages. Loyal customers.
Operators use flock3 teammates to find energy waste, predict outages and churn, and triage tickets. Engineers approve every change.
91.7%churn prediction accuracy
27%fewer tickets with smart triaging
3x+return on triaging investment
The challenge
What's in the way
Energy is the biggest network cost
Equipment and site logs hold the signals of waste, but nobody reads them at scale.
Churn spotted too late
Retention offers go out too late or to the wrong customers.
Tickets bounce between teams
Tickets pass through several agents before reaching the right team, raising cost and response times.
Focus use cases
What we automate
Energy Utilization from Log Data (Rakuten)
Read equipment and network logs to find energy waste and anomalies.
Network Operations
Anomaly detection and predictive maintenance to prevent outages.
Customer Churn Prediction
Predict churn and trigger retention offers before customers leave.
Smart Ticket Triaging
Route tickets by sentiment, urgency, topic and language.
Also in the libraryWorker safetyAI barcode scannerFraud mitigationUsage-based rewardsIntelligent CRM
Teammate swarm
Network Energy Swarm
Log Analyst AgentReads equipment and network log data
Forecast AgentPredicts load and energy demand per site
Anomaly AgentFlags waste and failing equipment
Ops Planner AgentRecommends power-saving actions and tickets
Human gateNOC engineer approves configuration changes
KPIskWh per site · outage minutes · tickets avoided
From the IntelliMesh use-case lab
More, ready to go
Voice Support Automation
NLP and LLM-based speech tools streamline backend operations and IT support.
Built on acceleratorsVoice AI Agents · Predictive Forecasting & Time-Series · Agentic AI & Multi-Agent Orchestration · Enterprise RAG & Knowledge
Use cases and accelerators co-developed with our US partner Mintmore Inc. See mintmore.us.
Case study · Telecom
Customer churn prediction
Telecom operator
Operators lose 15–25% of customers a year. We tested models on 7,043 customers. The winner feeds retention campaigns.
0.97recall
0.92precision
Inside the PDF (10 pages, 4.0 MB)3-page case study: problem, approach, impact and the teammate swarm7 slides from the IntelliMesh.si Platform Overview and Teammate Swarm decksHow we engage: the 10·20·30 Pathway
Start the 10·20·30 Pathway
Prove it on your Telecom data in 20 days
Other industries
Explore other industries
Questions
Telecom AI, answered.
How does AI predict telecom churn?
Models learn from call records, complaints and network logs. The best one is selected automatically and feeds retention campaigns with the subscribers most likely to leave.
How can AI cut telecom network energy use?
The Energy Analyst teammate reads equipment log data to find energy waste and anomalies across the network, as in Vera's work with Rakuten.