Case Study

Dermal Spa (Guam) — Website + AI Voice Agent

Client: High-end skincare clinic offering 110+ services

Challenge

High call volume and manual scheduling caused missed bookings and operational chaos.

Solution

  • Next.js website with booking system and MongoDB/Supabase backend
  • Admin dashboard for daily booking management
  • AI voice agent using Retell AI that identifies callers and books via n8n

Result

  • 24/7 booking coverage
  • Increased appointment conversion rate
  • Decreased front desk workload by 60%

Stack

Next.js, Supabase, Retell AI, n8n, Twilio API

💬 Case Study: Booking Platform + AI Voice Agent for a Dermal Skin Care Spa (Guam)

We built a complete booking ecosystem — website, booking system, admin dashboard, and an AI voice agent — to eliminate missed calls and automate operations.

📍 Client Overview

Client: Dermal Skin Care Spa (via Hexona Systems, outsourcing partner)

Industry: Health & Beauty / Wellness Automation

Services Provided: Website, Booking System, Admin Dashboard, AI Voice Booking Agent, SEO

🚀 The Challenge

Manual phone bookings, missed calls, no centralized data, and a website that underperformed in local search — despite offering 110+ premium services.

💡 The Objective

Build a web platform, admin interface, and an AI voice agent that books appointments automatically — while handling 100+ services and real‑time availability.

⚙️ The Technical Build

1) Website & Booking Platform

  • Next.js website optimized for speed and SEO
  • Dynamic services engine for 110+ treatments via Supabase
  • Smart booking form with email/SMS confirmations
  • Admin dashboard to view/update daily bookings and histories
  • Hybrid DB: Supabase (users) + MongoDB (fast booking queries)

2) AI Voice Booking Agent

  1. Caller greeted by Retell AI agent
  2. n8n workflow checks availability in Supabase
  3. Agent confirms and books automatically
  4. Complex requests alert admins via Telegram

Natural language handled via speech‑to‑intent mapping for conversational booking.

3) SEO & Local Discovery

  • Rich metadata and schema for services
  • Google Business Profile and local content optimization
  • Sub‑1.2s page loads for better ranking

📊 The Results

MetricBeforeAfter
Appointment SystemManual callsFull web + AI automation
Missed Calls~25% dailyNear zero
Admin Booking Time3–4 hours/day15–20 minutes/day
Local Search Traffic<10%53% from Guam searches

🏗️ Tech Stack Summary

Frontend: Next.js, TailwindCSS · Backend: Supabase, MongoDB · Automation: n8n, Telegram API · AI: Retell AI, Twilio · SEO: Schema + GSC

🧭 Conclusion

Combining voice AI with a scalable booking platform eliminated missed calls and manual overhead while improving local discovery.

Contact: hi@aidaptics.com

Objectives

  • Define clear KPIs and success metrics
  • Automate the highest‑leverage workflows first
  • Ensure data integrity across integrated systems

Outcomes

  • Operational time savings and higher conversion
  • Real‑time visibility for teams and stakeholders
  • Scalable architecture for future growth
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