AI-Powered Churn Prevention Platform

Built on AWS Cloud Infrastructure with ScikIQ Intelligence

Platform Architecture Overview

This world-class AI-powered churn prevention platform combines AWS cloud services, ScikIQ intelligence, and Omantel business logic to deliver a comprehensive customer retention solution with 90%+ prediction accuracy and 7-14 day advance warning.

AWS Service Map - Multi-Layer Architecture

Layer 1: Presentation & Interaction
AWS Elastic Beanstalk (Flask)
AWS CloudFront CDN
Bootstrap 5 UI
Voice Call Simulator
Workflow Builder
Churn Dashboard
Data Agent Mapper
Layer 2: AI Agent Orchestration (A2A/ACP)
18 Specialized Agents
AWS Lambda (Serverless Agents)
Agent-to-Agent Communication
ACP 1.0.0 Protocol
AWS Step Functions (Pipeline)
AWS SNS/SQS (Message Bus)
Layer 3: MCP Tool Layer & AI Services
13 MCP Tools
Amazon Bedrock (GPT-4 / Claude)
AWS SageMaker (AutoML)
Amazon Polly (TTS)
Amazon Transcribe (STT)
Amazon Comprehend (NLP)
Amazon Kendra (Search)
Layer 4: Data & Storage
Amazon RDS (PostgreSQL)
Amazon DynamoDB (Customer Profiles)
Amazon S3 (Data Lake)
AWS Glue (ETL & Data Catalog)
Amazon Athena (SQL Queries)
Amazon Redshift (Analytics)
10 Dataset Categories
Call Transcripts (S3)
Intervention History (DynamoDB)
Layer 5: Real-time Data Streaming
Amazon Kinesis Data Streams
Amazon Kinesis Firehose
Amazon SQS (Message Queues)
Amazon SNS (Pub/Sub)
AWS IoT Core (Device Events)
Amazon MSK (Kafka)
Real-time Analytics
Layer 6: Integration & APIs
Amazon API Gateway
AWS AppSync (GraphQL)
Amazon Connect (Voice)
Amazon EventBridge (Events)
ServiceNow Integration
Salesforce CRM
AWS Lambda (Webhooks)

100% AWS Cloud Deployment Architecture

Regional Infrastructure (Middle East)
AWS Route 53 (DNS)
AWS CloudFront (CDN - me-south-1 edge)
AWS WAF (Security)
AWS Shield (DDoS Protection)
Region: me-south-1 (Bahrain) + Local Zones (Muscat)
Compute & Orchestration
AWS ECS/EKS (Containers)
AWS Lambda (Serverless)
AWS Fargate (Serverless Containers)
AWS Step Functions (Workflows)
AWS Batch (ML Training)
Auto Scaling Groups
AI/ML Services
Amazon Bedrock (LLM)
AWS SageMaker (ML)
Amazon Comprehend (NLP)
Amazon Polly (TTS)
Amazon Transcribe (STT)
Amazon Kendra (Search)
Data Tier (me-south-1)
Amazon Aurora (Multi-AZ - Bahrain)
Amazon DynamoDB (Regional)
Amazon S3 (Regional + Cross-Region Replication)
Amazon Redshift (Bahrain Warehouse)
Amazon ElastiCache (Redis - Bahrain)
AWS Glue (ETL - me-south-1)
Security & Monitoring
AWS IAM (Access Control)
AWS KMS (Encryption)
AWS CloudWatch (Monitoring)
AWS X-Ray (Tracing)
AWS CloudTrail (Audit)
AWS Secrets Manager

Hybrid Cloud Deployment (AWS + On-Premise)

Connectivity Layer
AWS Direct Connect (10 Gbps)
AWS VPN Gateway (Backup)
AWS Transit Gateway (Hub)
AWS PrivateLink (Services)
Omantel MPLS Network
AWS Zone (Cloud)
VPC (me-south-1 - Bahrain)
AWS Outposts (Muscat, Oman)
Lambda + ECS (Compute)
Aurora + DynamoDB (Data)
Bedrock + SageMaker (AI)
S3 + Glacier (Storage)
On-Premise Zone (Omantel Data Center)
Core Banking Systems
Legacy CRM (On-Prem)
Oracle Database (Replicated)
Call Center Infrastructure
Customer Data (Regulatory)
On-Prem Kafka Cluster
Data Synchronization
AWS DataSync (File Sync)
AWS DMS (Database Migration)
AWS Storage Gateway (Hybrid Storage)
Kafka Connect (Streaming)
AWS Snowball (Bulk Transfer)
Real-time CDC (Change Data Capture)
Security & Compliance
End-to-End Encryption (TLS 1.3)
Customer Data Residency (Oman)
AWS Config (Compliance)
Unified IAM (AD Federation)
Local Data Sovereignty

End-to-End Data Flow Architecture

Stage 1: Data Ingestion
Customer Interactions (CRM, IVR, Web, Mobile)
โ†’ Amazon Kinesis Data Streams
โ†’ Amazon SQS Queues
โ†’ AWS IoT Core (Events)
โ†’ API Gateway (REST/GraphQL)
Stage 2: Data Storage & Cataloging
Raw Data โ†’ S3 (Data Lake)
Metadata โ†’ AWS Glue Data Catalog
Real-time โ†’ DynamoDB Streams
Transactional โ†’ Amazon Aurora
Archival โ†’ S3 Glacier
Stage 3: Data Processing & Enrichment
AWS Glue ETL (Batch Processing)
Lambda Functions (Event Processing)
Amazon Athena (SQL Queries)
Feature Engineering (SageMaker)
NLP Enrichment (Comprehend)
Text Extraction (Textract)
Stage 4: AI/ML Processing
Feature Selection Agent (Lambda)
Churn Prediction Model (SageMaker)
Sentiment Analysis (Comprehend)
Root Cause Analysis (Bedrock)
Offer Optimization (Reinforcement Learning)
Risk Scoring (Real-time)
Stage 5: Analytics & Insights
Amazon Redshift (Data Warehouse)
QuickSight (Dashboards)
CloudWatch (Operational Metrics)
Customer 360 View (DynamoDB)
Intervention Dashboard (Real-time)
Stage 6: Action & Orchestration
Step Functions (Workflow Orchestration)
SNS (Notifications)
Amazon Connect (Outbound Calls)
Pinpoint (Omnichannel)
Lambda (CRM Integration)
Feedback Loop โ†’ Kinesis
Billing Systems
๐Ÿ“Š Billing & Revenue Assurance (BRA) โ†’ Amdocs/Oracle BRM
๐Ÿ’ณ Payment Gateway โ†’ Omantel Payment Portal
๐Ÿ“ˆ Charging Data Records (CDR) โ†’ Mediation Platform
๐Ÿ’ฐ Invoice Management โ†’ SAP FICA
๐Ÿ”„ Dunning & Collections โ†’ Debt Management System
โ†’ AWS DMS (Change Data Capture) โ†’ S3 + RDS
Customer Lifecycle Management
๐ŸŽฏ Retention Team CRM โ†’ Salesforce/Oracle Siebel
๐Ÿ“ฑ Acquisition & Onboarding โ†’ Customer Activation Portal
โš ๏ธ Churn Analytics System โ†’ In-House ML Platform
๐ŸŽ Loyalty & Rewards โ†’ Comarch BSS
๐Ÿ‘ค Customer Master Data โ†’ MDM Hub (Informatica)
โ†’ API Gateway + Lambda โ†’ DynamoDB + S3
Contact Center & Servicing
๐Ÿ“ž Call Center Platform โ†’ Genesys/Avaya IVR
๐Ÿ’ฌ Chat & Messaging โ†’ LivePerson/WhatsApp Business API
๐Ÿ“ง Email Ticketing โ†’ ServiceNow
๐ŸŽง Voice Analytics โ†’ NICE inContact
๐Ÿ“‹ Case Management โ†’ Pega CRM
โ†’ Amazon Connect + Kinesis Streams โ†’ S3
Network & OSS/BSS
๐ŸŒ Network Management System (NMS) โ†’ Ericsson/Nokia OSS
๐Ÿ“ก Service Assurance โ†’ HP/BMC Remedy
๐Ÿ”ง Order Provisioning โ†’ Oracle Communications Order & Service Management
๐Ÿ“Š Quality of Service (QoS) โ†’ Netscout/EXFO
๐Ÿ›ฐ๏ธ Mobile Core (EPC/5GC) โ†’ Huawei/Ericsson
โ†’ AWS IoT Core + Kinesis โ†’ S3 + Timestream
Analytics & Digital Channels
๐Ÿ“ฑ Mobile App Analytics โ†’ Firebase/Amplitude
๐ŸŒ Web Portal โ†’ Omantel Self-Service Portal
๐Ÿ“Š Campaign Management โ†’ Adobe Campaign/Marketo
๐Ÿ” Search & Recommendations โ†’ Elasticsearch
๐Ÿ“ˆ BI & Reporting โ†’ Tableau/Power BI
โ†’ CloudFront + API Gateway โ†’ S3 + Athena

Data Integration Layer

Real-time CDC: AWS DMS (Database Migration Service) + Debezium
Batch ETL: AWS Glue + Step Functions
API Integration: Amazon API Gateway + Lambda + EventBridge
File Transfer: AWS DataSync + S3 Transfer Acceleration
Message Queue: Amazon SQS (FIFO) + Amazon MQ (ActiveMQ/RabbitMQ)
Event Streaming: Amazon Kinesis Data Streams + MSK (Kafka)
Data Catalog: AWS Glue Data Catalog + Lake Formation

Billing Data Ingestion
๐Ÿ’พ Oracle BRM Database โ†’ AWS DMS (CDC)
๐Ÿ“„ Invoice Files (PDF/XML) โ†’ S3 Bucket (s3://billing-raw/)
๐Ÿงพ CDR Files โ†’ SFTP โ†’ AWS Transfer Family โ†’ S3
๐Ÿ’ณ Payment Events โ†’ Kafka โ†’ MSK โ†’ Kinesis
๐Ÿ“Š Dunning Status โ†’ REST API โ†’ API Gateway โ†’ Lambda
Billing Data Storage
Raw Layer: S3 (Parquet/ORC format)
Structured Layer: RDS PostgreSQL (billing_warehouse schema)
Analytics Layer: Redshift (fact_billing, dim_invoice tables)
Fast Access: DynamoDB (customer_billing_summary)
Metadata: Glue Data Catalog (billing_catalog database)
Billing Data Processing
CDR Aggregation: Glue ETL Job (daily_cdr_rollup)
Invoice Parsing: Lambda + Textract (extract_invoice_data)
Revenue Recognition: Step Functions (revenue_calculation_workflow)
Payment Reconciliation: Lambda (reconcile_payments)
Anomaly Detection: SageMaker (billing_anomaly_model)
Billing Analytics & Insights
๐Ÿ“Š Revenue Trends: Redshift + QuickSight
๐Ÿ’ฐ ARPU Analysis: Athena + Lambda
โš ๏ธ Payment Delays: CloudWatch Alarms + SNS
๐Ÿ” Churn Correlation: SageMaker (late_payment_churn_model)
๐Ÿ“ˆ Predictive Billing: Bedrock (forecast_next_bill)
Billing-Triggered Actions
Late Payment Alert โ†’ SNS โ†’ SMS/Email via Pinpoint
High Bill Notification โ†’ Lambda โ†’ Amazon Connect (Proactive Call)
Payment Reminder โ†’ EventBridge Rule โ†’ Step Functions
Disputed Charges โ†’ ServiceNow API (Create Case)
Bill Shock Prevention โ†’ Real-time Athena Query โ†’ Push Notification
Customer Data Ingestion
๐Ÿ‘ค CRM (Salesforce) โ†’ MuleSoft โ†’ API Gateway โ†’ Lambda
๐Ÿ“ฑ Mobile App Events โ†’ Firebase โ†’ Kinesis Firehose โ†’ S3
๐ŸŒ Web Portal โ†’ CloudFront Logs โ†’ S3 โ†’ Athena
๐ŸŽฏ Loyalty System โ†’ SOAP/REST API โ†’ AppSync โ†’ DynamoDB
๐Ÿ“‹ KYC Documents โ†’ Upload โ†’ S3 + Comprehend (PII Detection)
Customer 360 Data Model
Profile: RDS (customer_profile: name, contact, demographics)
Subscriptions: DynamoDB (active_services, plan_details, add-ons)
Behavior: S3 + Athena (usage_patterns, app_interactions)
Sentiment: DynamoDB (nps_score, csat, sentiment_history)
Churn Risk: SageMaker Endpoint (real-time_churn_score)
Customer Data Enrichment
Segmentation: Lambda (rfm_analysis, lifecycle_stage)
Propensity Scoring: SageMaker (upsell_propensity, device_upgrade)
Social Listening: Comprehend (brand_sentiment_analysis)
Location Intelligence: AWS Location Service (geo_analysis)
Graph Analysis: Neptune (network_effects, referral_graph)
Customer Analytics
๐ŸŽฏ Acquisition Funnel: Redshift + QuickSight
โ™ป๏ธ Retention Cohorts: Athena + Python (pandas)
๐Ÿ“‰ Churn Drivers: SageMaker Autopilot (feature_importance)
๐Ÿ’Ž CLV Modeling: SageMaker (lifetime_value_prediction)
๐Ÿ” Root Cause Analysis: Bedrock (why_customers_leave)
Retention & Acquisition Actions
๐ŸŽ Personalized Offers โ†’ Offer Engine (Lambda) โ†’ Pinpoint
๐Ÿ“ž Retention Call โ†’ Step Functions โ†’ Amazon Connect
๐Ÿ“ง Win-back Campaign โ†’ SES + EventBridge
๐ŸŽฏ Next Best Action โ†’ Bedrock (personalization_agent) โ†’ CRM Update
๐Ÿ”” Churn Alert โ†’ SNS Topic โ†’ Retention Team Dashboard
Network Data Ingestion
๐Ÿ“ก Cell Tower Logs โ†’ Ericsson OSS โ†’ SFTP โ†’ S3
๐ŸŒ 5G Core Events โ†’ Kafka โ†’ MSK โ†’ Kinesis Data Streams
๐Ÿ“Š QoS Metrics โ†’ SNMP Traps โ†’ IoT Core โ†’ Timestream
๐Ÿ”ง Fault Management โ†’ HP Remedy โ†’ API Gateway โ†’ Lambda
๐Ÿ“ˆ Performance Counters โ†’ NetScout โ†’ CloudWatch Agent โ†’ CloudWatch
Network Data Storage
Time-Series: Amazon Timestream (network_metrics_db)
Raw Logs: S3 (s3://network-logs/ - Parquet)
Aggregated: Redshift (fact_network_kpi, dim_cell_tower)
Real-time: DynamoDB (network_status_cache)
Geospatial: S3 + Athena (GeoJSON format)
Network Data Processing
Call Drop Analysis: Lambda (cdr_drop_detection)
Coverage Mapping: SageMaker (signal_strength_heatmap)
Capacity Planning: Glue ETL (traffic_forecasting)
Anomaly Detection: Lookout for Metrics (network_anomaly_detector)
Root Cause Analysis: Bedrock (rca_agent)
Network Issue Impact
๐Ÿ“‰ Poor QoS โ†’ Churn Risk Increase (Lambda โ†’ Update DynamoDB)
๐Ÿ“ž Call Drops โ†’ Customer Dissatisfaction (SNS โ†’ Retention Alert)
๐ŸŒ Slow Data Speed โ†’ Complaint Prediction (SageMaker)
๐Ÿšซ Service Outage โ†’ Proactive Notification (Step Functions)
๐Ÿ“Š Network Quality Score โ†’ Customer 360 View (API Update)
Proactive Network Actions
๐Ÿ”ง Auto-ticket Creation โ†’ ServiceNow API (Lambda)
๐Ÿ“ข Service Degradation Alert โ†’ Pinpoint (SMS Broadcast)
๐ŸŽ Apology Credit โ†’ Billing System API (Auto-compensation)
๐Ÿ“ž Proactive Outreach โ†’ Amazon Connect (IVR Notification)
๐Ÿ“Š Network Health Dashboard โ†’ QuickSight (Real-time)
Interaction Data Ingestion
๐Ÿ“ž Call Recordings โ†’ Genesys โ†’ S3 + Transcribe
๐Ÿ’ฌ Chat Logs โ†’ LivePerson โ†’ Kinesis Firehose โ†’ S3
๐Ÿ“ง Email Tickets โ†’ ServiceNow Webhook โ†’ API Gateway โ†’ Lambda
โญ CSAT Surveys โ†’ SurveyMonkey API โ†’ EventBridge โ†’ S3
๐Ÿ“ฑ App Feedback โ†’ In-App SDK โ†’ Pinpoint โ†’ S3
Interaction Data Storage
Call Transcripts: S3 (s3://interactions/call-transcripts/)
Sentiment Scores: DynamoDB (interaction_sentiment_table)
Case History: RDS (case_management schema)
Voice Analytics: S3 + Athena (emotion_detection, keywords)
Interaction Timeline: DynamoDB (customer_journey_stream)
Interaction Intelligence
๐ŸŽญ Sentiment Analysis: Comprehend (real-time_sentiment_api)
๐Ÿท๏ธ Topic Extraction: Comprehend (topic_modeling)
๐Ÿ˜ก Complaint Classification: SageMaker (complaint_classifier)
๐Ÿ” Intent Detection: Lex V2 (nlu_intent_parser)
๐Ÿ“Š Agent Performance: Lambda (qa_scoring, handle_time_analysis)
Contact Center Analytics
๐Ÿ“ž First Call Resolution: Athena (fcr_analysis)
โฑ๏ธ Average Handle Time: CloudWatch Metrics
๐Ÿ˜ค Escalation Patterns: Redshift (escalation_analysis)
๐ŸŽฏ Contact Reason Trends: QuickSight Dashboard
๐Ÿ”ฅ Agent Coaching Opportunities: SageMaker (agent_feedback_model)
Interaction-Driven Actions
๐Ÿ˜ก Angry Customer Alert โ†’ SNS โ†’ Manager Escalation
๐ŸŽ Complaint Compensation โ†’ Offer Engine โ†’ Auto-apply Credit
๐Ÿ“ž Callback Scheduling โ†’ Amazon Connect (queue_callback)
๐Ÿค– Chatbot Deflection โ†’ Lex + Bedrock (self-service_resolution)
๐Ÿ“Š Post-Interaction Survey โ†’ Pinpoint (nps_survey_trigger)

Agentic Architecture - World-Class AI Orchestration

Stage 1: Detection & Trigger (Agents 1-4)
1๏ธโƒฃ Feature Selection Agent โ†’ S3 + Athena
2๏ธโƒฃ Analytics Agent โ†’ Lambda + SageMaker
3๏ธโƒฃ Data Labeller Agent โ†’ Bedrock + DynamoDB
4๏ธโƒฃ Text-to-SQL Agent โ†’ Bedrock + RDS
โ†“ SNS Topic: "CustomerFeatureExtracted"
Stage 2: Analysis & Prediction (Agents 5-6)
5๏ธโƒฃ NLP Sentiment Analyzer โ†’ Comprehend + Bedrock
6๏ธโƒฃ Predictive Model Agent โ†’ SageMaker Endpoint
AWS Step Functions: Parallel Execution
Churn Score โ†’ DynamoDB Stream
โ†“ EventBridge Rule: "ChurnPredictionComplete"
Stage 3: Risk Assessment (Agents 7-8)
7๏ธโƒฃ Risk Detection Agent โ†’ Kinesis + Lambda
8๏ธโƒฃ Root Cause Agent โ†’ Bedrock (Claude 3)
Real-time Monitoring: CloudWatch Events
High-Risk Alert โ†’ SNS โ†’ SQS Priority Queue
โ†“ SQS Queue: "HighRiskCustomers"
Stage 4: Intervention Strategy (Agents 9-11)
9๏ธโƒฃ Intervention Strategy Agent โ†’ Lambda + Bedrock
๐Ÿ”Ÿ Channel Orchestration Agent โ†’ Step Functions
1๏ธโƒฃ1๏ธโƒฃ Conversation Agent โ†’ Bedrock + Connect
3-Tier Offer Selection (ScikIQ Algorithm)
โ†“ EventBridge: "InterventionTriggered"
Stage 5: Execution (Agents 12-13)
1๏ธโƒฃ2๏ธโƒฃ Optimization Agent โ†’ SageMaker RL
1๏ธโƒฃ3๏ธโƒฃ Dialer Agent โ†’ Amazon Connect
Outbound Call โ†’ Polly (TTS)
Live Transcript โ†’ Transcribe (STT)
โ†“ Kinesis: "CallOutcome"
Stage 6: Feedback & Learning (Agents 14-18)
1๏ธโƒฃ4๏ธโƒฃ Churn Attribution Agent โ†’ Athena + QuickSight
1๏ธโƒฃ5๏ธโƒฃ Predictive Analytics Agent โ†’ SageMaker Autopilot
1๏ธโƒฃ6๏ธโƒฃ Sentiment Analysis Agent โ†’ Comprehend Custom
1๏ธโƒฃ7๏ธโƒฃ Competitor Intelligence โ†’ Kendra + Bedrock
1๏ธโƒฃ8๏ธโƒฃ Realtime Monitoring โ†’ Kinesis Analytics
Model Retraining โ†’ SageMaker Pipelines

18 AI Agents - Complete Ecosystem

Stage 1 (Data): Feature Selection, Analytics, Data Labeller, Text-to-SQL
Stage 2 (Analysis): NLP Sentiment Analyzer, Predictive Model
Stage 3 (Risk): Risk Detection, Root Cause Analysis
Stage 4 (Strategy): Intervention Strategy, Channel Orchestration, Conversation
Stage 5 (Execution): Optimization, Dialer
Stage 6 (Learning): Churn Attribution, Predictive Analytics, Sentiment Analysis, Competitor Intelligence, Real-time Monitoring

Agent Communication Protocol (ACP v1.0.0)

100% Message Delivery Rate
Directed Graph Communication
Real-time Agent-to-Agent (A2A)
ACP 1.0.0 Compliant

18-Agent Pipeline: Multi-Stage Orchestration

๐Ÿ”
Feature Selection Agent
Scans feature store, identifies critical columns, connects to Analytics Agent
Stage 1 ScikIQ AI ACP Enabled
๐Ÿ“Š
Analytics Agent
Samples 100-200 records, determines 10-20 KPIs using reasoning models
Stage 1 ScikIQ AI A2A
๐Ÿท๏ธ
Data Labeller Agent
Auto-generates churn risk labels, sentiment tags, complaint classifications
Stage 1 Active Learning
๐Ÿ’พ
Text-to-SQL Agent
Converts KPI definitions to SQL, enriches dataset with ~20 KPI columns
Stage 1 NL2SQL GPT-4
๐ŸŽญ
NLP Sentiment Analyzer
Analyzes call transcripts for sentiment, intent, emotion; creates weighted churn propensity scores
Stage 2 Deep NLP
๐ŸŽฏ
Predictive Model Agent
Ranks all customers by predicted churn score; selects top 50-100 high-risk customers
Stage 2 AutoML 90% Accuracy
โš ๏ธ
Risk Detection Agent
Continuously monitors behavior for 7-14 day advance detection
Stage 3 Real-time 7-14 Days
๐Ÿ”ฌ
Root Cause Agent
Deep dive into WHY customer wants to churn; analyzes usage, billing, interaction history
Stage 3 Causal AI
๐Ÿ’ก
Intervention Strategy Agent
Selects personalized three-tier offers (Floor/Middle/Ceiling) based on CLV and churn drivers
Stage 4 3-Tier Offers CLV-Based
๐Ÿ“ก
Channel Orchestration Agent
Optimizes delivery channel and timing; manages 15-minute SLA compliance
Stage 4 15-min SLA
๐Ÿ’ฌ
Conversation Agent
Real-time AI assistance during live customer calls; next-best-action recommendations
Stage 4 Real-time AI GPT-4
๐ŸŽ“
Optimization Agent
Learns from outcomes to improve future predictions; continuous model refinement
Stage 5 Reinforcement Learning
โ˜Ž๏ธ
Dialer Agent
Automated outbound calling with intelligent retry logic; SLA tracking
Stage 5 AWS Connect
๐Ÿ“‰
Churn Attribution Agent
Post-mortem analysis of churned customers
Support Attribution AI
๐Ÿ”ฎ
Predictive Analytics Agent
Future trend forecasting and scenario planning
Support Time Series
๐Ÿ˜Š
Sentiment Analysis Agent
Multi-touchpoint sentiment monitoring
Support Emotion AI
๐ŸŽฏ
Competitor Intelligence Agent
Market intelligence and counter-offer generation
Support Market Intel
๐Ÿ“ก
Realtime Monitoring Agent
Stream processing and anomaly detection
Support Real-time Streaming
ACP Protocol Specification
๐Ÿ“‹ Protocol Specification: ACP 1.0.0 (ScikIQ Standard)
๐Ÿ”„ Communication Pattern: Agent-to-Agent (A2A)
๐Ÿ“Š Message Format: JSON Schema with Type Safety
๐Ÿ” Security: TLS 1.3 + IAM Role-based Access
โœ… Delivery Guarantee: Exactly-Once Semantics
AWS Implementation of A2A Communication
Message Bus: Amazon SNS (Pub/Sub Pattern) - Fan-out to Multiple Subscribers
Queue Layer: Amazon SQS FIFO - Guaranteed Order & Exactly-Once Delivery
Workflow Engine: AWS Step Functions - State Machine Orchestration
Event Router: Amazon EventBridge - Event-Driven Architecture with Rules
Stream Processing: Kinesis Data Streams - Real-time Agent Communication
Data Exchange: DynamoDB Streams + S3 Events - Change Data Capture
A2A Communication Patterns
๐ŸŽฏ Direct A2A: Agent 1 โ†’ SQS โ†’ Agent 2 (Point-to-Point)
๐Ÿ“ข Broadcast A2A: Agent 1 โ†’ SNS Topic โ†’ All Subscribers (Fan-Out)
โšก Event-Driven A2A: Agent 1 โ†’ EventBridge โ†’ Rule-Based Routing
๐Ÿ”„ Stream A2A: Agent 1 โ†’ Kinesis Stream โ†’ Consumer Agents
๐ŸŽฌ Orchestrated A2A: Step Functions Workflow โ†’ Sequential/Parallel Agents
๐Ÿ“Š Data-Driven A2A: DynamoDB Change โ†’ Stream โ†’ Downstream Agents
ACP 1.0.0 Protocol Features
โœ… Message Versioning: Schema Evolution with Backward Compatibility
๐Ÿ”’ Authentication: AWS IAM + STS Temporary Credentials
๐Ÿ“ Message Schema: JSON Schema Validation (ajv library)
๐Ÿ” Retry Logic: Exponential Backoff with Dead Letter Queue (DLQ)
๐Ÿ“Š Observability: CloudWatch Metrics + X-Ray Tracing
โšก Priority Queues: High/Medium/Low Priority SQS Queues
ACP Performance Metrics
๐Ÿ“ˆ Message Throughput: 10,000+ messages/second (SNS + SQS)
โฑ๏ธ End-to-End Latency: < 100ms (P99)
โœ… Delivery Rate: 100% (Exactly-Once Semantics with FIFO)
๐Ÿ”„ Agent Availability: 99.99% (Multi-AZ Deployment)
๐Ÿ“Š Message Ordering: 100% Guaranteed (SQS FIFO)
๐ŸŽฏ Error Rate: < 0.01% (With Auto-Retry to DLQ)

Why A2A & ACP is World-Class

AWS Native: Built on AWS managed services (SNS, SQS, EventBridge, Step Functions) - No infrastructure management
ScikIQ Innovation: ACP 1.0.0 protocol with proprietary message schemas and agent orchestration patterns
Enterprise-Grade: 100% message delivery, exactly-once semantics, distributed tracing, multi-AZ resilience
Scalability: Auto-scales to millions of messages, handles burst traffic, supports 1000+ concurrent agents
Observability: Full visibility with CloudWatch, X-Ray distributed tracing, EventBridge audit logs
Cost-Optimized: Pay-per-use pricing, no idle costs, automatic resource optimization

Dataset Categories (10 Total)
๐Ÿ“Š Customer Demographics โ€ข Usage Patterns โ€ข Billing History โ€ข Network Quality
๐Ÿ’ฌ Customer Interactions โ€ข Sentiment Scores โ€ข Churn Labels โ€ข Competitor Data
๐ŸŽฏ Offers & Campaigns โ€ข Agent Performance Metrics
Dataset-Agent Mapping: Stage 1 (Data Preparation)
1๏ธโƒฃ Feature Selection Agent: Customer Demographics, Usage Patterns, Billing History, Network Quality
2๏ธโƒฃ Analytics Agent: Usage Patterns, Billing History, Customer Interactions
3๏ธโƒฃ Data Labeller Agent: Churn Labels, Customer Demographics
4๏ธโƒฃ Text-to-SQL Agent: All Datasets (Query Translation)
Storage: S3 Data Lake, RDS PostgreSQL, Athena, Glue Data Catalog
Dataset-Agent Mapping: Stage 2-3 (Analysis & Risk)
5๏ธโƒฃ NLP Sentiment Analyzer: Customer Interactions, Sentiment Scores
6๏ธโƒฃ Predictive Model Agent: Customer Demographics, Usage Patterns, Billing History, Network Quality, Sentiment Scores
7๏ธโƒฃ Risk Detection Agent: Usage Patterns, Billing History, Network Quality
8๏ธโƒฃ Root Cause Agent: All Datasets (Comprehensive Analysis)
Storage: DynamoDB (Real-time), Redshift (Analytics), S3 (Feature Store)
Dataset-Agent Mapping: Stage 4-5 (Strategy & Execution)
9๏ธโƒฃ Intervention Strategy Agent: Customer Demographics, Offers & Campaigns, Churn Labels
๐Ÿ”Ÿ Channel Orchestration Agent: Customer Interactions, Sentiment Scores
1๏ธโƒฃ1๏ธโƒฃ Conversation Agent: Customer Interactions, Sentiment Scores, Offers & Campaigns
1๏ธโƒฃ2๏ธโƒฃ Optimization Agent: Offers & Campaigns, Agent Performance Metrics
1๏ธโƒฃ3๏ธโƒฃ Dialer Agent: Customer Demographics (Contact Info)
Storage: DynamoDB (Customer 360), S3 (Call Recordings), RDS (Campaigns)
Dataset-Agent Mapping: Stage 6 (Feedback & Learning)
1๏ธโƒฃ4๏ธโƒฃ Churn Attribution Agent: Churn Labels, Customer Interactions, Offers & Campaigns
1๏ธโƒฃ5๏ธโƒฃ Predictive Analytics Agent: All Datasets (Model Retraining)
1๏ธโƒฃ6๏ธโƒฃ Sentiment Analysis Agent: Customer Interactions, Sentiment Scores
1๏ธโƒฃ7๏ธโƒฃ Competitor Intelligence Agent: Competitor Data, Offers & Campaigns
1๏ธโƒฃ8๏ธโƒฃ Realtime Monitoring Agent: Agent Performance Metrics, Usage Patterns
Storage: S3 (Historical), Kinesis Analytics (Streaming), Redshift (Reporting)

AWS Data Architecture for Agents

Data Lake: Amazon S3 (Raw/Processed/Curated Zones) - Petabyte-scale storage
Data Catalog: AWS Glue Data Catalog - Central metadata repository for all 10 datasets
Real-time Access: DynamoDB - Sub-millisecond latency for agent queries
Analytics: Amazon Redshift - Complex aggregations and reporting
Query Engine: Amazon Athena - Serverless SQL on S3
Feature Store: SageMaker Feature Store - Versioned ML features with low-latency access
Streaming: Kinesis Data Streams - Real-time dataset updates
Access Control: Lake Formation - Fine-grained permissions per agent role

Stage 1: Detection & Trigger (Agents 1-4)
1๏ธโƒฃ Feature Selection Agent โ†’ S3 + Athena
2๏ธโƒฃ Analytics Agent โ†’ Lambda + SageMaker
3๏ธโƒฃ Data Labeller Agent โ†’ Bedrock + DynamoDB
4๏ธโƒฃ Text-to-SQL Agent โ†’ Bedrock + RDS
โ†“ SNS Topic: "CustomerFeatureExtracted"
Stage 2: Analysis & Prediction (Agents 5-6)
5๏ธโƒฃ NLP Sentiment Analyzer โ†’ Comprehend + Bedrock
6๏ธโƒฃ Predictive Model Agent โ†’ SageMaker Endpoint
AWS Step Functions: Parallel Execution
Churn Score โ†’ DynamoDB Stream
โ†“ EventBridge Rule: "ChurnPredictionComplete"
Stage 3: Risk Assessment (Agents 7-8)
7๏ธโƒฃ Risk Detection Agent โ†’ Kinesis + Lambda
8๏ธโƒฃ Root Cause Agent โ†’ Bedrock (Claude 3)
Real-time Monitoring: CloudWatch Events
High-Risk Alert โ†’ SNS โ†’ SQS Priority Queue
โ†“ SQS Queue: "HighRiskCustomers"
Stage 4: Intervention Strategy (Agents 9-11)
9๏ธโƒฃ Intervention Strategy Agent โ†’ Lambda + Bedrock
๐Ÿ”Ÿ Channel Orchestration Agent โ†’ Step Functions
1๏ธโƒฃ1๏ธโƒฃ Conversation Agent โ†’ Bedrock + Connect
3-Tier Offer Selection (ScikIQ Algorithm)
โ†“ EventBridge: "InterventionTriggered"
Stage 5: Execution (Agents 12-13)
1๏ธโƒฃ2๏ธโƒฃ Optimization Agent โ†’ SageMaker RL
1๏ธโƒฃ3๏ธโƒฃ Dialer Agent โ†’ Amazon Connect
Outbound Call โ†’ Polly (TTS)
Live Transcript โ†’ Transcribe (STT)
โ†“ Kinesis: "CallOutcome"
Stage 6: Feedback & Learning (Agents 14-18)
1๏ธโƒฃ4๏ธโƒฃ Churn Attribution Agent โ†’ Athena + QuickSight
1๏ธโƒฃ5๏ธโƒฃ Predictive Analytics Agent โ†’ SageMaker Autopilot
1๏ธโƒฃ6๏ธโƒฃ Sentiment Analysis Agent โ†’ Comprehend Custom
1๏ธโƒฃ7๏ธโƒฃ Competitor Intelligence โ†’ Kendra + Bedrock
1๏ธโƒฃ8๏ธโƒฃ Realtime Monitoring โ†’ Kinesis Analytics
Model Retraining โ†’ SageMaker Pipelines

Inter-Agent Communication (ACP 1.0.0)

Message Bus: Amazon SNS (Pub/Sub) + Amazon SQS (Queuing)
Workflow Engine: AWS Step Functions (State Machine)
Event Routing: Amazon EventBridge (Event-Driven)
Data Exchange: DynamoDB Streams + S3 Events
Real-time Sync: Kinesis Data Streams
Agent Registry: AWS AppConfig + Parameter Store
Message Delivery: 100% Guaranteed (SQS FIFO + Dead Letter Queues)

AWS Excellence: Enterprise-Grade Infrastructure
โ˜๏ธ 100% AWS Native: Zero third-party dependencies - All managed services (Bedrock, SageMaker, Lambda, Step Functions)
๐ŸŒ Global Scalability: Auto-scales to millions of customers - Multi-region deployment (me-south-1 primary)
๐Ÿ”’ Security & Compliance: IAM, KMS encryption, VPC isolation, CloudTrail auditing - SOC 2, GDPR ready
๐Ÿ“Š Observability: CloudWatch 360ยฐ monitoring, X-Ray distributed tracing, real-time dashboards
๐Ÿ’ฐ Cost Optimization: Pay-per-use, Spot instances for training, auto-scaling, S3 lifecycle policies
โšก Performance: Sub-second agent response, < 100ms A2A latency, 99.99% availability
ScikIQ Innovation: Proprietary IP & Domain Expertise
๐Ÿค– ACP 1.0.0 Protocol: Proprietary agent communication standard - 100% message delivery, exactly-once semantics
๐ŸŽฏ 3-Tier Offer Engine: ScikIQ algorithm for personalized offer generation - 45% higher acceptance rate
๐Ÿ“ก Telecom Expertise: 15+ years domain knowledge - Omantel, Ooredoo, du implementations
๐Ÿง  18-Agent Orchestration: Multi-stage pipeline with feedback loops - Continuous learning & optimization
๐Ÿ”ฎ Early Warning System: 7-14 day advance churn prediction - 90%+ accuracy with custom models
๐Ÿ“Š 10 Dataset Categories: Comprehensive data coverage - Customer 360 view with network quality, sentiment, behavior
Industry-Leading Capabilities
๐Ÿ† MCP 1.0.0 Compliance: 100% Anthropic standard - 13 tools across 8 categories, future-proof architecture
๐Ÿ”„ Agent-to-Agent (A2A): Directed graph communication - SNS/SQS/EventBridge/Step Functions orchestration
๐ŸŽญ Multi-Language Support: Arabic + English NLP - Amazon Comprehend custom models, bidirectional translation
๐Ÿ“ž Voice AI: Amazon Polly (TTS) + Transcribe (STT) - Real-time conversation with emotion detection
๐Ÿ” Root Cause Analysis: Bedrock Claude 3 Opus - Natural language explanations for every churn prediction
โ™ป๏ธ Continuous Learning: Feedback loop from all 18 agents - SageMaker Pipelines automated retraining
Proven Business Impact
๐Ÿ“‰ Churn Reduction: 35% decrease in voluntary churn - Measured across 500K+ customers
๐Ÿ’ฐ Revenue Protection: $12M+ ARR saved - Retention interventions with optimized offers
โฐ Early Detection: 7-14 day advance warning - Proactive outreach before cancellation
๐ŸŽฏ Intervention Success: 62% save rate - Customers retained after targeted intervention
๐Ÿ“Š Model Accuracy: 90%+ churn prediction F1-score - Continuously improving with feedback loops
โšก Operational Efficiency: 80% automation - Reduced manual analysis from 40 hrs/week to 8 hrs/week
Technical Differentiators
๐Ÿ”ง Hybrid Architecture: Supports 100% AWS + AWS/On-premise hybrid - Direct Connect, VPN, Outposts integration
๐ŸŒŠ Real-time Streaming: Kinesis, MSK, DynamoDB Streams - Event-driven architecture with < 1 second latency
๐Ÿ—„๏ธ Multi-Tier Storage: S3 Data Lake + RDS + DynamoDB + Redshift + Timestream - Optimized for each use case
๐Ÿงช A/B Testing: SageMaker Experiments - Continuous offer optimization with RL algorithms
๐Ÿ” Data Residency: me-south-1 (Bahrain) + Oman Local Zones - Compliance with local data regulations
๐Ÿ“ฑ Omnichannel: Voice, SMS, Email, WhatsApp, Web - Amazon Connect, Pinpoint, SES, Chime

World-Class Recognition

AWS Partner: AWS Advanced Tier Partner - AI/ML Competency, Certified Solutions
ScikIQ Patents: 3 pending patents on ACP protocol, 3-Tier Offer Engine, Multi-Agent Orchestration
Industry Awards: Best AI Innovation (Telecom Tech Awards 2024), Top 10 Churn Solutions (Gartner)
Customer Success: Deployed at Omantel (Oman), Ooredoo (Qatar), du (UAE) - 2M+ customers
Academic Recognition: Published research on A2A communication patterns (IEEE 2024)
Compliance: SOC 2 Type II, ISO 27001, GDPR, Oman Data Protection Law

AI-Powered Contact Center Journey

Watch how 18 AI agents work together to detect at-risk customers, understand why they want to leave, and help your retention team save them with personalized offers. Experience the complete journey below.

Interactive Journey Playback

Click play to watch Ahmed's retention journey unfold step-by-step

Ready to start - Click "Start Journey" above
๐Ÿ‘จโ€๐Ÿ’ผ

Meet Ahmed Al-Rashid

A 3-year loyal customer with postpaid + home internet subscription. Recently experiencing network issues in Al Khuwair. Payment was 2 days late for the first time ever. Data usage dropped 40%. He's thinking about switching to Ooredoo...

High-Value Customer Churn Risk Detected 3 Years Tenure
๐Ÿ”
STEP 1

Early Detection

AI agents constantly monitor customer behavior - usage patterns, billing history, call quality complaints, and payment delays.

Ahmed's Story:
AI noticed Ahmed experienced 5 call drops last week in Al Khuwair. His data usage dropped by 40%. Payment was 2 days late (first time ever).
โšก
STEP 2

Risk Classification

AI analyzes WHY customers might churn and calculates their importance (CLV). Customers are classified into priority tiers.

Ahmed's Classification:
๐Ÿ”ด Tier 1 - Critical Priority. High-value customer (3 years, postpaid + home internet). Root cause: Network quality issues.
๐Ÿ“‹
STEP 3

Smart Queue Creation

At-risk customers are organized into priority queues. High-value customers get immediate attention from your best retention agents.

Ahmed's Queue:
Added to "Critical Outbound Queue" - Top retention specialist Sara will call him within 2 hours.
๐Ÿ“ž
STEP 4

Proactive Outreach

AI automatically dials the customer and connects them to a retention specialist. For inbound calls (customer calling to cancel), they skip the queue entirely.

The Call:
Sara receives the call with Ahmed's complete history on her screen. AI shows recommended talking points and offers.

During The Conversation

๐Ÿค–
AI Script Assistant
What It Does:
AI writes personalized talking points in real-time based on the customer's history and current mood.

AI Suggestion:
"Hi Ahmed, I see you've been experiencing network issues in Al Khuwair. We've already upgraded the tower there. I want to personally ensure your experience improves and discuss some exclusive offers..."
๐Ÿ˜Š
Live Emotion Tracking
What It Does:
AI listens to tone of voice and words to detect emotions in real-time.

Current Emotion: ๐Ÿ˜Š Satisfied
Sentiment:
Positive
๐Ÿ’ก AI Tip: Great time to present offer!
๐ŸŽ
3-Tier Offer System
What It Does:
AI prepares 3 offers (low/medium/high) based on customer value. Agent presents them in order.

๐Ÿฅ‰ Basic: 10% discount + 5GB data
๐Ÿฅˆ Standard: 20% discount + 10GB + router
๐Ÿฅ‡ Premium: 30% discount + 20GB + device
๐ŸŽ™๏ธ
Speech & Text Processing
What It Does:
Converts speech to text in real-time (Arabic & English). AI reads every word to help the agent.

Customer:
"I'm thinking of switching to Ooredoo..."
โš ๏ธ Competitor mentioned - suggest counter-offer

After The Call

โœ…

Customer Saved

Offer accepted โ†’ Auto-applied to account โ†’ Confirmation SMS sent โ†’ 7-day follow-up scheduled

โฐ

Needs More Time

Callback scheduled โ†’ AI sets reminder โ†’ Different agent may call โ†’ Offer validity extended

๐Ÿ“Š

Still Churned

AI analyzes WHY they left โ†’ Updates churn model โ†’ Improves future predictions โ†’ Learns from failure

Why This Is Revolutionary

Early Warning System

Traditional contact centers react AFTER customers complain. Our AI detects problems 7-14 days in advance, before customers even think about leaving.

Real-Time Intelligence

Agents don't work alone - AI assists during every call with smart scripts, emotion detection, and instant offer recommendations.

Continuous Learning

Every call makes the system smarter. AI learns which offers work best, which talking points resonate, and how to handle objections.

Human + AI Collaboration

AI handles detection, analysis, and recommendations. Human agents bring empathy, judgment, and relationship-building skills.

The Complete Ecosystem

18 AI Agents working together across 8 stages to power both inbound (customer calling to cancel) and outbound (proactive retention) operations.

From the moment a customer shows early warning signs, to the final outcome tracking and learning - every step is powered by AI, guided by human expertise, and optimized for customer satisfaction.

Proof of Concept: From Reactive to Predictive

Transform Omantel's Post2Pre churn prevention from manual intervention after outlet visits to AI-powered prediction 7-14 days before customers leave.

5%

Churn Reduction Target

75%+

Prediction Accuracy Goal

0%

Revenue Cannibalization

The Challenge: Reactive Retention is Too Late

Current State Reality
  • 5,000 customers churn annually (0.8% Post2Pre rate)
  • 70% retention rate with manual interventions
  • 15-minute response window after outlet task creation
  • Generic offer selection not tied to churn drivers
  • No predictive capability - reaction only after visit
Why Manual Processes Fail
  • Cannot predict who will churn before outlet visit
  • Cannot diagnose why they want to switch
  • CRM shows "Next Best Offer" but no reasoning
  • Floor โ†’ Middle โ†’ Ceiling escalation lacks personalization
  • No systematic learning from retention outcomes

The Core Problem: By the time a customer walks into an outlet to switch to prepaid, their decision is mostly made. Manual AON-based CLV segmentation and reactive Floor/Middle/Ceiling offers are too generic and too late. You're fighting to save customers who are already halfway out the door.

The POC Solution: Agentic AI Prediction & Personalization

This POC augments your existing retention workflow with 7 Specialized AI Agents powered by AWS Bedrock that work together to predict, diagnose, and personalize retention offers before customers decide to leave.

๐Ÿ”
Risk Detection Agent

Analyzes 24-month behavioral patterns to predict P2P churn 7-14 days before outlet visit. Monitors usage drops, payment delays, roaming/tariff churn signals.

๐Ÿงฉ
Root Cause Analysis Agent

Diagnoses WHY customer wants to switch. Analyzes transaction reasons, complaint history, bill shock, competitor influence to create churn driver profile.

๐ŸŽฏ
Intervention Strategy Agent

Recommends personalized Floor/Middle/Ceiling offers based on churn drivers. Maps offers to reasons, aligns with business rules, predicts acceptance probability.

โšก
Channel Orchestration Agent

Optimizes 15-minute response window. Prioritizes high-risk customers, validates SLA compliance, plans escalation paths, integrates with CRM+ task queue.

POC Enhancement: Instead of generic AON-based offers, AI provides churn-driver-specific Floor/Middle/Ceiling recommendations ready for retention agents within your existing 15-minute window. Same workflow, smarter decisions.

Expected Business Impact

๐Ÿ“‰
Reduce Churn Rate

0.8% โ†’ 0.76%

5% reduction in Post2Pre churn through proactive intervention

๐Ÿ“ˆ
Increase Retention

70% โ†’ 73.5%

5% improvement through personalized offers

๐Ÿ’ฐ
Zero Revenue Loss

0%

Optimized offers prevent cannibalization

Critical Data Requirements (24-Month Minimum)

AI agents require historical data to learn patterns. The more complete the data, the better the predictions. Below is what we need from Omantel to deliver accurate 7-14 day early warnings.

Customer Profile & Account Data
  • Customer ID, Age, Gender, Geographic location
  • Customer type (individual, corporate, VIP)
  • Current plan type (postpaid plan details)
  • Activation date, Age on Network (AON)
  • Current status (active, suspended, terminated)
  • Contract details: start/end dates, plan, add-ons
Billing & Revenue Data
  • Monthly billing history (24 months minimum)
  • Net revenue, ARPU (Average Revenue Per User)
  • Discounts, offers applied, tax breakdown
  • Outstanding balances, overdue days, dunning stage
  • Payment history, bill shock indicators
Usage & Consumption Data
  • Voice minutes (incoming/outgoing)
  • Data usage (MB/GB)
  • SMS count
  • Roaming usage and charges
  • Add-on services usage patterns
Customer Interaction Data
  • Call center/outlet interactions (date, channel, resolution)
  • Complaint/ticket data: ID, category, severity, SLA
  • Agent notes or reason codes for dissatisfaction
  • App usage (login frequency, features used)
Retention Offers & Campaigns
  • Catalog of Floor/Middle/Ceiling offers
  • Historical offers: type, date, accepted/rejected
  • Retention period achieved after offer
  • Offer cost to company (for ROI calculations)
Churn & Migration Labels
  • Customer churn events (postpaid โ†’ prepaid) with dates
  • Transaction reasons from CRM+ data
  • Return-migration events (prepaid โ†’ postpaid)
  • "Reasons for No" when offers rejected

Why 24 Months? AI needs to see patterns across seasons, promotional periods, and lifecycle stages. Customers who churned 12-24 months ago provide the "training data" to predict future churners. More history = Better predictions.

RACI: Who Does What

Clear accountability ensures POC success. Here's exactly who is responsible for each deliverable.

Deliverable / Activity Omantel AWS SciKiq
24-Month Historical Data Provision R I A
CRM+ Integration & API Access R C A
AWS Bedrock Setup & Infrastructure I R A
Agentic AI Development (7 Agents) C C R
Business Rules & Offer Catalog R I A
Model Training & Optimization C C R
User Acceptance Testing (UAT) R I A
Retention Agent Training R I A
Success Metrics Validation A C R
R

Responsible
(Does the work)

A

Accountable
(Final approval)

C

Consulted
(Provides input)

I

Informed
(Kept updated)

Beyond POC: The Agentic Future

This POC is your launchpad. Once we prove AI can predict Post2Pre churn, the same framework unlocks unlimited possibilities across your entire business.

Expand to All Churn Types

Post2Pre is just one type of churn. The same agents can predict postpaid โ†’ competitor, prepaid โ†’ abandonment, home internet disconnection. One AI framework, infinite applications.

Flip to Revenue Growth

Risk Detection Agent becomes Opportunity Detection Agent. Instead of churn signals, detect upsell signals. Launch 5G upgrades, home internet bundles, device financingโ€”all with the same predictive engine.

Add New Agents Instantly

Need a Fraud Detection Agent? Network Quality Agent? VIP Experience Agent? Plug them into the existing framework. AWS Bedrock makes adding new agents a configuration change, not a rebuild.

Scale Across Omantel

Proven POC โ†’ Pilot with 10,000 customers โ†’ Production with all postpaid base โ†’ Expand to prepaid, home, enterprise, B2B. Same AI, same agents, enterprise-wide impact.

The Reusable AI Framework

You're not buying a "Post2Pre churn solution." You're building a reusable agentic AI platform that can solve any customer lifecycle problem. Today: prevent churn. Tomorrow: grow revenue. Next month: optimize operations. One investment, unlimited ROI.

From Idea to Impact in Minutes

Watch your business transform as 18 AI Agents and 77 Tools orchestrate customer experiences that were impossible yesterday.

7-14

Days Early Detection

100%

Automated Workflows

โˆž

Journey Possibilities

0

Code Required

Live Customer Journeys

Churn Prevention: The Ahmed Story

It's Monday, 3:42 AM. While your team sleeps, AI just saved a customer worth $2,400.

โœ“ LIVE NOW
๐Ÿ‘จโ€๐Ÿ’ผ
Ahmed's Journey from Risk to Retention

12 days ago: Ahmed, a 3-year customer paying $200/month, started showing subtle warning signs. Network quality dropped. Data usage fell 40%. Payment was 2 days late for the first time ever.

โš ๏ธ Day 1 (3:42 AM): Real-time Monitoring Agent detected anomaly. Risk Detection Agent calculated 87% churn probability. Root Cause Agent identified: network quality in Al Khuwair area.

๐ŸŽฏ Day 2 (Morning): Offer Strategy Agent analyzed Ahmed's history: loves data, responds well to discounts. Generated 3-tier offer: Standard (20% off + 10GB), Premium (30% off + router), Exclusive (40% off + VIP support).

๐Ÿ“ž Day 3 (10:15 AM): Queue Orchestration Agent prioritized Ahmed as Tier 1 Critical. Communication Agent drafted personalized message referencing his exact issue. Your best retention agent Sara received the call task with full context.

๐Ÿ’ฌ During Call: Sentiment Analysis Agent detected Ahmed's frustration at 2:15 mark. Emotion Detection suggested empathy response. When sentiment improved to 68% positive, Communication Agent prompted Sara: "Present Standard offer now."

โœ… Day 3 (10:32 AM): Ahmed accepted 20% discount + 10GB bonus + router upgrade. Confirmation SMS sent automatically. Billing updated. Network priority escalated. Customer saved. $2,400 annual value retained.

๐Ÿ”
Early Detection

Monitor behavior patterns 24/7

๐ŸŽฏ
Risk Scoring

Predict churn probability using ML

๐Ÿค–
AI Orchestration

18 agents work together seamlessly

โœ…
Customer Saved

Personalized offers close the deal

AGENTS POWERING THIS JOURNEY
Real-time Monitoring Risk Detection Root Cause Analysis Offer Strategy Communication Sentiment Analysis +12 more...
THE INVISIBLE ORCHESTRA: 18 AGENTS WORKING IN PERFECT HARMONY

Ahmed never knew that Real-time Monitoring, Risk Detection, Root Cause Analysis, Offer Strategy, Queue Orchestration, Communication, Sentiment Analysis, and Emotion Detection agents were working 24/7 to save his account. They used 77 specialized toolsโ€”analyzing billing history, predicting churn probability, generating retention offers, detecting emotions in real-time, sending SMS confirmationsโ€”all orchestrated via ACP Protocol without a single line of code from your team.

๐Ÿ’ฐ THE BUSINESS CASE

Before AI: You'd lose Ahmed. He'd visit an Ooredoo outlet, cancel his $2,400/year account, and you'd never know why until it was too late.

With AI Orchestration: You saved him 9 days before he could cancel. This journey runs 24/7, automatically, across thousands of customers.

๐ŸŽฏ REPLICATING SUCCESS

This exact journey saved Ahmed. Tomorrow it might be Fatima, or Mohammed, or any of your 50,000 customers showing early warning signs. No additional cost. No extra headcount. Just AI working silently in the background, 365 days a year.

5G Revenue Explosion: Launch Day

Your competitor launched 5G last week. You're launching tomorrow. AI just identified 12,847 perfect targets.

โšก CLICK TO ACTIVATE
THE CHALLENGE

Scenario: You're about to launch 5G in Muscat. You need to hit revenue targets fast. Traditional marketing would take 3 weeks to segment customers, design campaigns, get approvals, and launch. By then, competitors will have captured market share.

โšก AI DOES IT IN 4 HOURS
HOUR 1

Segment Selection: AI scans all customers in 5G areas with 5G devices + high data usage

HOUR 2

Propensity Scoring: ML predicts who's most likely to upgrade based on behavior

HOUR 3

Personalization: GenAI crafts 12,847 unique messages referencing customer history

HOUR 4

Launch: SMS, email, WhatsApp sent. Dashboard shows real-time uptake

๐Ÿ“ˆ RESULT: 847 upgrades in first 24 hours. $338,800 additional monthly revenue. Campaign cost: $0 in human hours.

๐ŸŽฏ THE "ART OF THE POSSIBLE" MOMENT

Traditional approach: 3 weeks, 10 people, generic messages to broad segments. AI approach: 4 hours, zero manual work, hyper-personalized messages to individually-scored targets. Same team size. 21x faster. Infinite scale. Tomorrow you can launch another campaign for home internet upgrades, or device financing, or international roamingโ€”using the exact same AI orchestration.

The 3PM Crisis: When AI Saves Your Reputation

Network outage hits Al Khuwair. 847 angry customers calling. Your 12 agents are drowning.

๐Ÿ”ฅ CRISIS MODE
๐Ÿ“ž CALL #237 AT 3:47 PM
๐Ÿ‘ฉ

Caller: Mariam, a VIP customer (CLV: $4,200/year). She's on hold for 8 minutes. Frustration level: extreme. She's already tweeting about switching to Ooredoo. Your junior agent Khalid picks upโ€”it's his 3rd week on the job.

โšก AI SPRINGS INTO ACTION (Milliseconds)

  • 0.2s: Customer profile loaded. Khalid sees: VIP status, 4-year tenure, network issue history, current outage affecting her area
  • 0.4s: Intent Detection Agent identifies: "cancel service" mentioned in first 10 seconds
  • 0.6s: Sentiment Analysis shows: Anger (92%), Frustration (88%)
  • 0.8s: Knowledge Agent retrieves: Outage resolution ETA, compensation policy, VIP retention protocol
  • 1.2s: Communication Agent suggests: "Empathize first. Acknowledge 8-min wait. Reference her loyalty. Offer immediate compensation"

โœ… OUTCOME (4 minutes later)

Khalid (guided by AI every step): apologized perfectly, explained outage transparently, offered 1 month free service + priority network support. Mariam's sentiment shifted from 92% anger to 78% satisfaction. She deleted her tweet. VIP customer retained. CSAT score: 4.5/5.

๐ŸŽฏ SCALE OF IMPACT

That day: 847 calls. 12 agents (6 of them new hires). AI guided every single conversation with real-time suggestions, knowledge retrieval, and sentiment monitoring. Zero customers churned. Average CSAT: 4.2/5 during a crisis.

๐Ÿ’ช AGENT SUPERPOWER

Without AI: Khalid would've panicked, put Mariam on hold to ask supervisors, fumbled through knowledge base, lost the customer. With AI: He performed like your best 10-year veteran. Instant expertise, perfect empathy, flawless execution.

The Art of the Possible

With 18 AI agents and 77 specialized tools, your business can create unlimited customer journeys. Here's what's possible:

Create New Journeys in Minutes

Use the visual workflow builder to drag-and-drop agents into custom sequences. Want to launch a "Win-Back Dormant Customers" campaign? Simply connect Dormancy Detection โ†’ Propensity Scoring โ†’ Multi-Channel Communication โ†’ Offer Optimization.

โšก Deploy in minutes, not months

Hybrid Decision Making

Choose where AI acts autonomously and where humans approve. For low-risk customers, agents can auto-approve offers. For VIP accounts, route to senior managers for final approvalโ€”all configurable per journey.

๐ŸŽฏ Balance automation with control

Stack Journeys for Complexity

Combine multiple journeys into sophisticated workflows. Run churn prevention AND upsell campaigns simultaneously. If a customer accepts the retention offer, automatically trigger cross-sell journey for accessories.

๐Ÿ”— Orchestrate complex scenarios effortlessly

Continuous Learning & Optimization

Every customer interaction feeds back into the AI. Agents learn which offers work best, optimal contact timing, effective messagingโ€”and automatically improve future decisions without manual retraining.

๐Ÿ“ˆ Performance improves daily, automatically

How Agents & Tools Enable Automation

18 Specialized AI Agents

Think of agents as digital employees with specific expertise:

  • Risk Detection Agent: Predicts churn 7-14 days early with >75% accuracy
  • Offer Strategy Agent: Calculates ROI-positive offers using historical data
  • Communication Agent: Generates personalized messages for each customer
  • Sentiment Analysis Agent: Detects emotion in real-time during calls
  • Queue Orchestration Agent: Prioritizes customers by value and urgency
  • ...and 13 more specialized agents

๐Ÿ’ก Agents talk to each other via ACP Protocolโ€”sharing insights without human coordination

77 Intelligent Tools

Tools are the "superpowers" that agents use to get work done:

  • analyze_billing_history: Scan 24 months of payment patterns
  • predict_churn_probability: ML model calculates 0-100% risk score
  • generate_retention_offer: Creates personalized discount bundles
  • detect_emotion_realtime: Analyzes voice tone and word choice
  • send_sms_confirmation: Delivers instant confirmation messages
  • ...72 more tools across 13 categories

๐Ÿ› ๏ธ Each agent uses multiple toolsโ€”like an employee using Excel, CRM, and email to complete tasks

Example: When the Risk Detection Agent spots a high-value customer showing churn signals, it uses predict_churn_probability and analyze_usage_patterns tools, then messages the Offer Strategy Agent via ACP Protocol. That agent runs calculate_offer_roi and sends results to the Communication Agentโ€”all in milliseconds, with zero human intervention.

Scale Without Scaling Costs

24/7
Always-On Operations

AI agents work continuouslyโ€”no shifts, breaks, or holidays. Scale from 100 to 100,000 customers with the same infrastructure.

80%
Task Automation Rate

Repetitive tasks like data analysis, queue management, and offer calculation happen automatically. Teams focus on relationship-building.

40%
Operational Cost Reduction

Reduce manual overhead, optimize resource allocation, and achieve more with smaller teams through intelligent automation.

Real Business Impact: Traditional contact centers scale linearlyโ€”double the customers, double the agents. With AI orchestration, you can 10x customer reach while increasing headcount by just 2x. The AI handles volume; humans handle complexity.

Build Your Own Journey

You don't need to be a data scientist or engineer. Our visual workflow builder empowers business users to create sophisticated AI-powered journeys.

1
Choose Trigger

Select what starts the journey: churn signal, new purchase, support ticket, or custom event

2
Add Agents

Drag agents onto canvas and connect them. Each agent brings specialized capabilities

3
Configure Rules

Set decision logic: if churn score > 70%, route to priority queue. If CLV < $50, auto-approve basic offer

โœ“
Deploy & Monitor

Activate journey with one click. Track performance in real-time dashboard. Iterate based on results

Example Use Cases You Can Build Today:

Payment Recovery Campaign Loyalty Rewards Fulfillment Network Issue Proactive Alerts Device Upgrade Recommendations Seasonal Promotion Campaigns VIP Customer Appreciation