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AI Receptionist & Appointment Booking System For Dental Clinic
Julia Dental — AI Receptionist & Appointment Booking System
I created an AI receptionist for Julia Dental, designed to help a dental clinic handle incoming calls and appointment requests without relying entirely on a human receptionist.
The AI receptionist can communicate with patients, answer common questions about the clinic and its services, collect patient information, and help schedule appointments. The goal is to make sure potential patients can get assistance even when the clinic staff is busy or unavailable.
Key features:
AI-powered phone receptionist
Patient questions and FAQs
Appointment booking assistance
24/7 call handling
Automated patient information collection
Reduced missed calls and potential lost appointments
Natural, conversational AI communication
This project demonstrates how AI can be integrated into a dental clinic’s daily operations to improve the patient experience, reduce repetitive administrative work, and help the clinic capture more appointment opportunities.
Technologies: AI / LLM, voice automation, workflow automation, appointment scheduling, API integrations
Demo project created to demonstrate the concept and functionality of an AI receptionist for dental clinics.
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The Head of Marketing at Clerkenwell Health told me their Squarespace site looked like a small clinic. They were doing clinical trials for 50,000+ participants, licensed by the Home Office, but their web presence was costing them credibility before the first call.
Pharma sponsors and CROs Google them before every partnership conversation. The site was creating a bad first impression, making conversations harder than they needed to be.
What they said they needed was a 'website refresh'. What they actually needed was to stop actively losing trust the moment a prospect hit their homepage.
We moved them to Framer, preserved 60 blog articles with full SEO, and built n8n automation for inquiry routing. Zero downtime on launch.
It's not just about a site update. It's about what happens when your external image no longer matches your internal scale. What are the silent costs when that gap gets too wide?
That’s a wise approach. I’m currently exploring the combination of n8n and Framer as well, especially around AI-powered workflows and web design. It’s interesting to see how these tools are coming together.
