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🎧 Customer Service🏗️ 4 Tech Levels🚀 Production-Grade

Voice & Chat Support AI

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

June 27, 2025
demointeractivevoice-supportchat-supportcustomer-service
🤖Demo 1 of 4

Advanced Multi-Agent System

BOFU: Production LangGraph handling 10K+ concurrent voice/chat sessions

1. Select Analysis Scenario

High-emotion query requiring empathy + billing expertise + quality validation
Competitors: Customer: 'I was charged twice this month!', Sentiment: Frustrated (0.85 negative), Intent: Billing dispute (0.92 confidence)

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 support tools via Model Context Protocol

Automation Task

User query → LLM decides tools needed → Calls check_order_status() + get_shipping_info() in parallel → Returns tracking info

Monitoring:Order #12345
ai-agent-terminal

Terminal ready. Run simulation to begin.

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🔍Demo 3 of 4

RAG-Based Knowledge Search

MOFU: Search 1000+ support docs, FAQs, tickets with vector similarity

Search Query

"Search Knowledge Base for Troubleshooting Steps"

Embed query → Search vectors → Retrieve top 5 chunks → LLM synthesizes troubleshooting steps

Document Sources:FAQ Article #234: Android App CrashesSupport Ticket #10234: Resolved Android crashProduct Guide: Android TroubleshootingInternal Wiki: Common Android Issues

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 Quality Monitoring

MOFU: Daily automation monitoring 100+ tickets for quality, sentiment, compliance

ROI Analysis

Orchestrator calls: Sentiment Analyzer(all_tickets) + Response Validator(all_tickets) + Compliance Checker(all_tickets) → Performance Reporter aggregates → Alert Tool notifies

Integrations:125 tickets analyzed4 compliance flags18 negative sentiment tickets92% quality score
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 Voice & Chat AI?

We'll build your custom support system: MCP tools (TOFU - answer queries) → RAG search (MOFU - search knowledge base) → Multi-tool (MOFU - daily quality monitoring) → Multi-agent (BOFU - 10K+ concurrent sessions). From demos to deployment in 8-12 weeks.

Book Architecture ReviewStart with MCP Tools (TOFU)
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2026 Randeep Bhatia. All Rights Reserved.

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