Skip to main content
📈 Growth🏗️ 4 Tech Levels🚀 Production-Grade

Retention System AI

See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent

October 24, 2025
demointeractiveretentiongrowthmulti-agent
🤖Demo 1 of 4

Advanced Multi-Agent Retention System

BOFU: Production LangGraph with Router → Specialized Agents → Aggregation

1. Select Analysis Scenario

User shows 3 churn signals: 60% engagement drop, failed payment, 2 support tickets. Router routes to all 3 specialists for comprehensive analysis.
Competitors: User ID: user_8472 (Enterprise plan, $499/mo, 8 months tenure), Engagement signals from Mixpanel, Billing data from Stripe

2. Monday's Prompt

This positioning analysis prompt from Monday's insight will be processed by Tuesday's agents in Wednesday's workflow.

3. Run Multi-Agent Workflow

You'll see 5 AI agents: Data Collector → Messaging Analyzer → Position Mapper → Strategy Advisor → Insight Generator
↓ Next Demo ↓
🔌Demo 2 of 4

MCP Tool Use Pattern

TOFU: LLM calls retention tools via Model Context Protocol - see real tool execution

Automation Task

User query: 'Is user_8472 at risk?' → LLM decides: need engagement + churn score + support context → Calls 3 tools in parallel → Returns risk assessment

Monitoring:user_8472
ai-agent-terminal

Terminal ready. Run simulation to begin.

↓ Next Demo ↓
🔍Demo 3 of 4

RAG-Based Retention Intelligence

MOFU: Search 100+ user behavior patterns with vector similarity

Search Query

"Search Similar Churn Patterns"

Embed query → Search vectors (find similar: API abandonment + billing issue) → Retrieve top 5 cases → LLM synthesizes intervention strategy

Document Sources:Case Study: user_3421 (saved with API troubleshooting + discount)Postmortem: user_7834 (churned despite discount - needed technical fix first)Interview: user_5621 (saved with dedicated integration support)Cohort Analysis: Q2 2024 API users (35% saved with proactive outreach)Success Playbook: API Abandonment Protocol (3-step intervention)

RAG Vector Search Network

Embedding
Searching
Generating
Query
Vector
Documents
Result

Search Results

Run visualization to see results

↓ Next Demo ↓
🛠️Demo 4 of 4

Multi-Tool Retention Orchestration

MOFU: Multiple retention tools working together automatically - daily monitoring

ROI Analysis

Orchestrator triggers daily run (6am) → Calls 5 tools in parallel: EngagementMonitor(all_users) + ChurnPredictor(all_users) + CohortAnalyzer(all_cohorts) + WinBackGenerator(high_risk_users) + AlertSystem(urgent_cases) → Aggregates results → Sends reports

Integrations:10,247 users monitored23 cohorts analyzed87 high-risk users identified23 urgent interventions generated5 Slack alerts sent to retention team
Manual Process

Time to Complete

240 min

Cost per Analysis

$850

Data Points Analyzed

150

Accuracy Rate

73.0%

Reports per Day

2

Error Rate

18.0%

Ready for Production Retention System?

We'll build your custom retention system: MCP tools (TOFU - try churn analysis) → RAG search (MOFU - search retention knowledge base) → Multi-tool (MOFU - daily automated monitoring) → Multi-agent (BOFU - adaptive 24/7 system). From demos to deployment in 8-12 weeks. Reduce churn 5% → 2%, preserve $200K+ annual MRR, free up 2.5 FTE analysts for strategic work.

Book Retention System ReviewStart with MCP Tools (TOFU)
©

2026 Randeep Bhatia. All Rights Reserved.

No part of this content may be reproduced, distributed, or transmitted in any form without prior written permission.