Booking and scheduling
8 released templates. Each passed a check on test data; it is switched on in the panel by the director, and a person decides on every proposal. Example results are illustrative: the names and companies in them are made up.
Booking assistant
The problem it removes: No-shows and empty slots are direct losses.
More detail
What it does: Offers customers open slots from the schedule export and drafts a booking or rescheduling reply.
What a person decides: Exceptions and prepayment.
For whom: Customer.
How to measure: Bookings made without the front desk.
Example result: “You're booked with Dr. Akhmetova on Tuesday, October 14, at 15:30. Address: 52 Abay St., second floor. I'll send a reminder the day before — if your plans change, just reply to it and we'll find another time.”
Visit reminders
The problem it removes: Customers forget their appointments.
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What it does: Prepares visit reminders from the bookings export with a simple “I'll come” or “reschedule” reply.
What a person decides: The final decision on the outcome.
For whom: Customer.
How to measure: No-shows.
Example result: “Aigerim, a reminder: tomorrow at 11:00 you have an appointment with Dr. Akhmetova, 52 Abay St. Reply ‘1’ if you're coming or ‘2’ if you need to reschedule, and we'll find another time.”
Waitlist
The problem it removes: A canceled slot stays empty.
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What it does: Offers the freed-up time to people on the waitlist.
What a person decides: Confirming the booking.
For whom: Customer.
How to measure: Freed-up slots refilled.
Example result: “Aliya, a slot just opened today at 16:00 with your stylist Dina — you asked for something earlier. Shall I book you? We'll hold it for 30 minutes, then offer it to the next person on the list.”
Booking confirmation
The problem it removes: Unconfirmed bookings turn into no-shows.
More detail
What it does: Asks customers to confirm the visit and records the answer.
What a person decides: The final decision on the outcome.
For whom: Customer.
How to measure: Share of confirmed visits.
Example result: “Hi Yerlan! We're expecting you tomorrow at 10:00 for a service at Avtodom, 15 Ryskulov St. Please confirm by replying ‘yes’ or ‘reschedule’ — if needed, we'll find another time.”
Specialist scheduling
The problem it removes: Doctors' and stylists' schedules live in spreadsheets.
More detail
What it does: Proposes a schedule based on workload and time off.
What a person decides: Approving the schedule.
For whom: Manager.
How to measure: Specialist utilization.
Example result: “Action: for November, give Bekov two Tuesday shifts instead of three plus one Saturday — in October Tuesdays were a third full, while on Saturdays we turned people away 6 times. Impact: same hours, more bookings. Risk: regular patients are used to Tuesdays — let them know. Fallback: bring Tuesday back in December.”
Return visit
The problem it removes: Customers don't come back for scheduled follow-ups.
More detail
What it does: Reminds customers when their next visit is due based on their history.
What a person decides: The reminder text.
For whom: Customer.
How to measure: Return visits.
Example result: “Hi Dinara! It's been six months since your professional cleaning — the clinic recommends this as a good time for the next one. We have openings Thursday at 17:00 and Saturday at 11:00 — which works better?”
Rescheduling when a specialist is sick
The problem it removes: A doctor is sick — and every customer has to be called by hand.
More detail
What it does: Offers customers a different specialist or a new time.
What a person decides: The rescheduling option.
For whom: Customer, front desk.
How to measure: Visits saved during illness.
Example result: “Hello, Aidos! Unfortunately Dr. Akhmetova is ill, so tomorrow's 15:00 appointment can't go ahead — sorry for the inconvenience. We can see you at the same time with Dr. Serikova, or move you to Dr. Akhmetova on Friday at 16:00. Which do you prefer?”
Resource booking
The problem it removes: Rooms, halls and equipment are booked in a jumble.
More detail
What it does: Manages bookings and warns about conflicts.
What a person decides: The final decision on the outcome.
For whom: Staff member.
How to measure: Booking conflicts.
Example result: “Action: move Kim's 15:00 appointment from room 2 to room 3 — room 2 has two bookings at 15:00 (Saparova and Kim), room 3 is free until 17:00 and has the same chair. Impact: both are seen on time. Risk: Kim's doctor may only work in room 2 — check. Fallback: move Kim to 16:00 in room 2.”
Basis: product documentation; the platform code. This page describes what exists in this build, not what is planned.