How to Automate Support & Appointment Booking Using LLMs (The Right Way)
A step-by-step use case for turning website visitors into qualified meetings using AI agents, calendar integration, and conversation memory
Most websites are still running on static forms or generic chatbots. But what if your AI agent could actually hold a useful conversation—and book appointments for your team while you sleep?
We recently designed this exact system for both clinics and B2B teams. And here’s what we learned about doing it right.
Why this matters
Whether you're a medical practice, law firm, or SaaS vendor, you probably face the same issue:
You want to reduce time spent answering repetitive questions
You want to convert more visitors into real leads
You need those leads pre-qualified and ready for a smooth first call
Modern LLMs let us get there—but only if the architecture is well thought out.
Here’s how we build it.
For tips on quickly testing your automation ideas, read Avoiding Costly AI Prototypes.
1. Start With Real Conversations as a Baseline
Don’t start from scratch. If you already have email or chat history from previous interactions with leads or patients, use it.
We’ve had great success by:
Example: A clinic had hundreds of email replies explaining consultation pricing and availability. We used these to give the agent a consistent tone and accurate responses.
2. Write the Right Prompt: Informative, Inviting, and Proactive
LLMs need boundaries and personality. Your system prompt should:
Prompt starter: “You are an assistant for a clinic who helps visitors understand our services and invites them to schedule an appointment. Your tone is warm, professional, and proactive. When the visitor seems interested, always suggest a call and offer to check our team’s availability.”
3. Add Calendar Integration to Book in Real-Time
No one likes filling a form and waiting for a call-back. We connect the agent to a Google Calendar (or Calendly-style tool with API access), so it can:
Pro tip: Use calendar metadata to segment types of appointments (e.g., “first consultation”, “support call”, “demo request”).
4. Store Conversations for Traceability and Context
All user-agent conversations should be saved in a structured way—especially when booking is involved.
We often use Supabase or PostgreSQL to:
5. Send Context Inside the Calendar Event
Here’s a detail most people miss: The agent already had a full conversation with the user. Why not use it?
When booking the meeting, the agent should:
This makes the first meeting smoother, and shows the user they’ve been listened to.
Final Reflection
This system works because it’s simple and focused.
You’re not trying to build an all-knowing chatbot. You’re building a conversion agent:
→ It informs
→ It books
→ It prepares your team to follow up
That’s more than most websites do—and it works 24/7.
We’ve built this for clinics, B2B vendors, and internal support desks. If you’re thinking about automating your first point of contact (without making it robotic), I’d love to swap notes.
To foster a broader innovation mindset in your company, see Why Embracing a Tech & Innovation Culture Is Vital.