getcitafy

Launching Citafy on Product Hunt soon

If you run a dental, aesthetic, or medical clinic, you already know the problem: a patient messages your WhatsApp, nobody answers fast enough, and they book with the clinic next door instead. It happens more than you'd think and it's costing you real patients every week.

Citafy turns your clinic's WhatsApp into an AI receptionist that never sleeps. It answers patient questions instantly, books appointments with zero scheduling conflicts, sends automatic reminders, quotes treatments, and re-engages patients who went quiet all 24/7, directly on the WhatsApp number you already use.

It's not here to replace your team. Anything sensitive or that needs human judgment gets handed straight to your staff you stay in full control of hours, pricing, and rules.

I've been building an AI that handles WhatsApp conversations for independent medical and dental clinics answering questions, quoting treatments, booking appointments, sending reminders.

The hardest design problem hasn't been the scheduling logic, it's been deciding exactly when the AI should stop and hand off to a human. Right now it escalates on anything sensitive or outside what the clinic configured, but I keep second-guessing where that line should sit in a trust-sensitive space like healthcare.

For anyone building agents in health, legal, or finance: how do you draw that line? Hard rules, confidence thresholds, or something else?

(Happy to share a quick demo in the comments if anyone's curious how it works in practice.)

I ve been thinking a lot about the difference between personalization and real adaptation in fitness software.

A profile with age, goal and experience level is useful, but it still treats each workout request almost like a new session.

A more interesting model is longitudinal: remember what the person actually completed, which movements and muscle groups were trained recently, how difficult the exercises felt, what restrictions exist, and how nutrition has looked over the previous days.

Then the next recommendation is based on that history instead of starting from zero.