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Behind the counters · AI Wait Predictor

Wait times you can say out loud, because they hold up.

The AI Wait Predictor learns how long each service takes at each counter and hour, from your own history. It gives every visitor an honest estimate, spots the counter falling behind and suggests what to move. Your staff decide.

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Predicted wait · City Hospital OPD

Accuracy ±3 min
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Dr. Iyer's room is 9 min behind after two long consultations. 6 patients will be told their new time.

Suggest opening Registration 3 from 11:30 to 12:30. Expected peak drops from 21 to 13 min.

Hourly wait forecast with two suggestions for the floor manager.

±3 min

Typical estimate range after 4 weeks

Every 30 s

Estimates refresh

0

Moves without staff approval

What it does

Prediction that works for the visitor and the floor manager.

Learns from your history

Service times by counter, service, staff and hour, adjusted for Saturdays, month-end, festival weeks and the first day after a holiday.

Per-visitor estimates

Each token gets its own estimate, updated as the queue moves. Visitors are told when it changes by more than five minutes.

Behind-pace alerts

When a counter drifts behind its usual pace, the floor manager sees it with the likely reason, such as several complex job cards.

Rebalancing suggestions

Move a group of tokens, open another counter or route a service elsewhere, with the expected effect on wait times shown first.

Staffing forecast

A day-ahead and week-ahead forecast of visitors per hour, so you plan shifts for the Saturday rush on Thursday.

Staff decide

Suggestions are just that. A person approves each one, and the decision is logged with their name.

How it runs

From first setting to a calm hall.

  1. 01

    Run as usual

    Twiz15 starts with your expected service times from day one.

  2. 02

    It learns quietly

    Each served token sharpens the model for that counter and hour.

  3. 03

    Suggestions appear

    After about two weeks, rebalancing suggestions start to show.

  4. 04

    You approve

    Accept, adjust or dismiss. Visitors are updated only after approval.

Fairness and responsible AI

The AI never quietly reorders anyone.

Queues follow the rules your business sets: first come first served, appointments and priority groups. The predictor works inside those rules. It can suggest that a group of tokens moves to a quieter counter, but it never jumps one visitor ahead of another.

  • Queue order is set by your rules, never by the model
  • Estimates are shown honestly, including when they get longer
  • Every suggestion shows its reason and its expected effect
  • Phone numbers are used only for queue updates and deleted on schedule
  • No personal data is used to predict service time

Rules first

First come first served, appointment slots and priority groups are fixed by you. The model sees them as hard limits.

Explained suggestions

'Counter 2 is 12 minutes behind because 3 of the last 4 job cards were complex.' Never a black box.

Your data stays yours

Models are trained per business. Data is encrypted in transit and at rest, and never sold or shared.

Questions

Before you switch it on.

  • No. It can suggest that a visitor be served at a different counter, but booked appointment times are never moved by the system. If staff change one, the visitor is told.

Empty seats, happy faces

Press for your token

Trial counter

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2 waiting in the hall · 31 waiting from home · average wait 11 min