Graphical design mode
Connect process steps on the canvas. Set arrivals, processing times, capacities, and schedules. See the flow and refine its assumptions directly.
Plan your operation
Design your model visually, or talk it through with an AI simulation specialist. Build models, interpret results, and get recommendations for changes to test before changing the real thing.
Explore three recorded examples without an account. Sign in to build and run your own models.
Connect process steps on the canvas. Set arrivals, processing times, capacities, and schedules. See the flow and refine its assumptions directly.
Describe your operation to an AI simulation specialist. Build and refine your model, ask what the results mean, and get recommendations for what to test next.
Move between the canvas and chat as you work. Review proposed model changes before applying them.
Scripted model-building demonstration
Describe the operation. Answer a clarifying question. Approve the assumptions, then watch the assistant build the flow.
Describe the operation
The model appears after the user approves the plan.
Build a walk-in clinic model. Patients arrive about every 10 minutes, queue for triage, then wait for a doctor and leave. Triage takes 2–7 minutes, usually 4. Doctor visits average 8 minutes.
How many triage nurses and doctors are available? How long should the simulation cover?
One triage nurse and one doctor. Model an eight-hour day, with the first hour as warm-up.
I’ll use one nurse, one doctor, an 8-hour run and 1-hour warm-up. For this example, I’ll assume exponential arrival and doctor-visit timing, and triangular triage timing. Review these assumptions: shall I build the model?
Yes, build it.
Building the reviewed model: arrivals, waiting lines, triage, doctor, and exit.
Your six-step model is ready to review and run. After a baseline run, ask me to explain the results and recommend a change to test.
This is an authored demonstration of the review-and-build workflow. Messages and model steps are saved with the page; nothing is submitted to chat or saved to a workspace.
Recorded simulation replay
Watch patients arrive, wait, receive care, and leave. The moving dots and counts replay a saved event timeline. This page does not run a new simulation.
Run an experiment to play the trace.
Would another room or clinician reduce waiting?
How should work be shared between people and AI?
How many people should be on duty at busy times?
Would another gate lane clear the truck queue?
Where do shared crews or equipment slow work down?
Draw your flow, adapt an example, or describe the operation to the assistant. Review the proposed model and its assumptions before running it.
Run the current setup, then ask the assistant to explain waiting times, resource pressure, completed work, and uncertainty. Inspect the evidence in charts and on the model.
Explore suggested changes to staffing, capacity, or schedules. Test one change against your baseline, compare the evidence, and share a PDF or Excel report.
Choose your level inside the workspace. You can change it at any time without changing your models or results.
Follow four guided tasks: check assumptions, run a baseline, explain the result, and test one change. Plain-language help explains terms as you use them.
Preserve a baseline, check your run settings, and compare one controlled change with its uncertainty.
Use compact run information and direct modelling controls. Inspect capacities, distributions, schedules, repeats, and random seeds.
Suppose you are deciding between another exam room and another doctor. Compare each option with the current setup. The invented numbers below show how a comparison can help you choose what to investigate; they are not calculated results or a staffing recommendation.
Illustration only · invented values
104min
Illustration band 94–114 min. 3 doctors · 2 exam rooms
71min
Illustration band 62–80 min. About 33 minutes shorter
89min
Illustration band 79–99 min. About 15 minutes shorter
These bands are a planning illustration of uncertainty around an average, not a range for individual patients. In a real run, repeated simulations estimate that uncertainty; input quality and model assumptions still need separate validation.
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