Week 2 Progress

Building the AI Communication Layer

From concept to working prototype: implementing voice and messaging with AI as the mediator

The ChallengeWhat Problem Are We Solving?

Small service businesses spend hours on phone calls and follow-ups

Customers expect immediate response but staff are often busy

Missed calls = missed revenue and frustrated customers

Goal: Let AI handle routine communication while preserving the human touch

Core ConceptAI as the Communication Mediator

Customer
Calls / Texts
→
AI Layer
Understands & Responds
→
Business
Gets Structured Data

AI doesn't replace the business — it translates between customer intent and business systems

Customers get natural conversation, business gets actionable data

DesignFive Communication Scenarios

Inbound
Booking Request
Customer calls to make an appointment
Inbound
Service Inquiry
Questions about services, pricing, hours
Outbound
Appointment Reminder
Confirm upcoming appointments
Outbound
Customer Recall
Re-engage customers who haven't visited
Outbound
Promotional Campaign
Personalized offers based on customer history and preferences

ImplementationThe Technology Stack

Voice
Twilio
→
Deepgram STT
→
OpenAI GPT-4
→
Deepgram TTS
SMS
Twilio SMS
→
OpenAI GPT-4
→
Twilio SMS
Email
AWS SES
→
AI Personalization
→
AWS SES

DemoWhat I Built This Week

🎙️
Voice Test Interface
Browser-based voice testing with real-time STT/TTS, scenario selection, and guest context
💬
Conversation Engine
Context-aware AI that remembers customer history and adapts responses
📊
Scenario Framework
Configurable scenarios with custom prompts, greetings, and business rules
🔗
System Integration
Connected to real customer database, services, and scheduling system

Looking AheadNext Steps

Week 3 Build the marketing experiment platform to A/B test AI vs human outreach
Week 4 Refine voice quality and add multi-turn conversation memory
Midterm End-to-end demo: customer calls, AI books appointment, confirmation sent
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