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🛍️ Retail🏗️ 4 Tech Levels🚀 Production-Grade

Omni-Channel Personalization AI

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

September 19, 2025
demointeractiveomni-channelpersonalizationretail
🤖Demo 1 of 4

Advanced Multi-Agent System

BOFU: Production LangGraph with Router → Specialized Agents → Aggregation

1. Select Analysis Scenario

Customer abandons $450 cart on website, opens mobile app 2 hours later. System detects context, personalizes experience.
Competitors: Customer: Sarah, 34, Premium member, $2,400 LTV, Cart: 3 items ($450 total), abandoned 2hrs ago, Context: Now on mobile app, browsing similar products

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
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🔌Demo 2 of 4

MCP Tool Use Pattern

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

Automation Task

User query → LLM decides tool (get_customer_profile) → Executes → Returns structured profile → LLM formats answer

Monitoring:Customer: Sarah (CUST-89234)
ai-agent-terminal

Terminal ready. Run simulation to begin.

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🔍Demo 3 of 4

RAG-Based Customer Intelligence

MOFU: Search 100+ customer profiles with vector similarity

Search Query

"Find Lookalike Customers - Build Campaign Audience"

Embed Sarah's profile → Search 100K+ customer vectors → Retrieve top 50 similar profiles → Generate audience insights

Document Sources:Customer Database: 127,843 profilesQuery Customer: Sarah (Premium, Sustainable Fashion, $2400 LTV)Retrieved: 50 similar customers

RAG Vector Search Network

Embedding
Searching
Generating
Query
Vector
Documents
Result

Search Results

Run visualization to see results

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🛠️Demo 4 of 4

Multi-Tool Orchestration

MOFU: Multiple tools working together automatically

ROI Analysis

Orchestrator calls: Cart Monitor (Shopify) → Profile Sync (CDP) → Recommendation Engine (ML) → Inventory Checker → Notification Sender → Slack Alert

Integrations:Shopify: 1,247 abandoned carts (last 24hrs)Segment CDP: Customer profiles enrichedML Model: Personalized offers generatedInventory: Real-time stock checkedNotifications: Sent via optimal channel
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 Omni-Channel System?

We'll build your custom system: MCP tools (TOFU) → RAG search (MOFU) → Multi-tool (MOFU) → Multi-agent (BOFU). From demos to deployment in 8 weeks. Real-time personalization across web, mobile, email, SMS, store. 24/7 autonomous operation.

Book Architecture ReviewStart with MCP Tools (TOFU)
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