Project Description

1. Approach to building the MVP
Start lean and validate fast:

Phase 1 (Core MVP)Handle missed calls → trigger AI callback or SMS
AI voice agent handles:Name capture
Reason for call
Appointment intent
Simple booking flow + confirmation
Store all interactions (calls, transcripts, bookings)
Keep logic simpleMostly rule-based flows + light AI
Avoid overengineering multi-agent systems early
Focus on reliabilityLow latency voice
Clean fallback (SMS if call fails)
Human handoff option

2. Suggested Tools / Stack
Voice & Telephony

Twilio or Vapi
(Vapi for faster AI voice setup, Twilio for control)
AI / LLM

OpenAI (GPT-4o for real-time voice + structured extraction)
Backend

Node.js (fast webhook handling)
Lightweight API (Express or Fastify)
Scheduling

Google Calendar API (simple + reliable)
Optional: Cal.com for booking layer
Database

Supabase (auth + DB + realtime logs)
Automation

Make.com or n8n (for quick workflows)
Hosting

Vercel (frontend) + Railway / Render (backend)

3. Similar Experience (Sample Answer Style)

Yes — worked on:

AI voice agents handling inbound calls (lead qualification + booking)
Missed-call → SMS + follow-up automation systems
SaaS tools using Twilio + GPT for real-time conversations
Booking + calendar sync systems with automated confirmations

Key focus in those projects:

Reducing latency in voice interactions
Structuring conversations for high completion rates
Ensuring fallback paths (SMS / human takeover)

Why this approach works
Fast to ship (1–2 weeks MVP possible)
Low cost + scalable
Easy to expand later (multi-language, CRM, reminders, etc.)
Designed for real-world reliability, not just demo