Velora Aesthetic Clinic Aesthetic medicine clinic, ul. Puławska 47, Mokotów, Warsaw, Poland

Business archetype and quirks A solo doctor-owner (Dr. Kasia Nowicka) with two practitioners, three treatment rooms and shared laser devices. There is no receptionist. 70% of inquiries arrive as Instagram DMs and WhatsApp messages asking "what would you recommend for me?" and "how much is it?". Evenings and Saturdays are overbooked, weekday mornings sit empty, no-show rate on consultations and first laser sessions is around 20%, clients often arrive unprepared for treatments with prep rules, and every reschedule happens by phone. Clients are a mix of Polish locals and expats.

The problem In a small clinic the same person treats patients, answers the phone and replies to DMs. When a patient is in the chair, the phone goes unanswered and messages pile up. Getting back to each inquiry means reading the message, understanding what the person actually needs, suggesting suitable treatments, and then answering a chain of follow-up questions about price, downtime, results and who performs it. That consultation-by-text takes far more time than the treatment itself, and half of those conversations never turn into a booking.

Problem solved (one line) Vela AI removes the owner from the inquiry stage entirely: it understands the client's need, recommends the right treatment, answers every question with real clinic data and books a paid appointment without a single human reply.

Links Lovable project (public): https://appvelora.lovable.app/ Admin demo login: demo@demo.com / Demo123 Demo video (under 3 min): [DEMO VİDEO LİNKİ] Process walkthrough (bonus): https://share.shotbase.com/330897b9ce134405b2cd62b43b910720 Social post (X): https://x.com/gemimatt/status/2105385986725208553

How it turns an inquiry into a confirmed booking Vela greets the client in Polish or English, asks about goals, skin and downtime tolerance, recommends the right treatment from the live catalog of 30 services, shows before/after galleries and expected results, introduces the best-suited practitioner with real experience and certifications, then lists genuinely open slots from the clinic calendar (practitioner and room both checked, booked slots never shown). The client taps a time in the chat, pays a deposit via Stripe, and gets an email and SMS confirmation with the prep rules for that exact treatment. A four-step classic booking flow runs on the same availability engine for clients who prefer not to chat.

How it reduces the owner's manual work Zero owner involvement in the normal path, from first question to paid booking. After booking, the system handles intake and consent forms, 48h, 24h and 3h reminders, self-service reschedule and cancel with a 24-hour cutoff, a waitlist that auto-offers freed slots, aftercare and rebooking emails, review requests, and an off-peak discount that pushes flexible clients into empty mornings. Deposits with a late-cancel policy target the no-show problem directly. In the admin panel the owner edits services, practitioners, the before/after library, message templates and the AI consultant's own instructions without code, and sees chat-to-booking conversion, no-show trend and off-peak fill rate on one dashboard.

Craft and execution Complete end to end: public site, AI consultant with tool calling, availability engine with double-booking protection at database level, Stripe payments, client portal, email and SMS automations on cron, full admin panel, seeded demo data and a demo mode with Stripe test cards. Built entirely in Lovable on Supabase, Stripe, Resend and Twilio, mobile-first with a premium clinic aesthetic.

Before and after Before: the owner switches between the treatment room and her phone all day, explains the same treatments over and over in DMs, loses half the leads to slow replies, chases confirmations by phone and absorbs 20% no-shows. After: every inquiry is answered instantly and fully by Vela, the client self-serves to a paid booking in one conversation, the calendar fills itself including off-peak hours, and the owner only opens the app in the morning to see who is coming, who confirmed and who is on the waitlist.