Things that arrive
Patients, calls, orders or cars. They show up at uneven times, not on a schedule.
Ask. Simulate. Decide.
Decision intelligence, powered by simulation
See the outcome before you commit.
Test your next multi-million-dollar decision before you make it. Describe it in plain words and see the expected result, with a range that shows how sure you can be.
Beta not open yet. Investor? Write to us
Clinic triage
You describe it
“A walk-in clinic with 2 triage nurses and 3 doctors. Simulate one day.”
Steps in the model
TriageTriage nurse x2
ConsultationDoctor x3
Decision tested
Add one more doctor
The wait for a doctor drops by 5.4 minutes (95% interval: 3.7 to 7.0 minutes).
Verdict, with 1 minute as the smallest gain worth acting on: real and big enough. The wait falls from 6.6 minutes to about 1.2 minutes. The whole interval is above 1 minute.
A clinic
- ArrivalsPatients arrive
- TriageTriage nurse x20.1 patients waiting on average
- Busiest: Doctor x3ConsultationDoctor x30.9 patients waiting on average
- DoneTime in system: 25.1 minutes95% interval: ± 1.9 minutes
Average wait at doctor
6.6 minutes
95% interval: ± 2.0 minutes
Doctor busy
73.8%
95% interval: ± 2.0 percentage points
Time in system
25.1 minutes
95% interval: ± 1.9 minutes
You describe it
“A support line with 13 agents and 3 supervisors. Simulate 10 hours.”
Steps in the model
Agent handles callAgents x13
Supervisor handles escalationSupervisors x3
Decision tested
Add one more agent
The wait for an agent drops by 9.5 seconds (95% interval: 7.9 to 11.1 seconds).
Verdict, with 10 seconds as the smallest gain worth acting on: real, but too close to call. The wait falls from 17.9 seconds to about 8.4 seconds. The interval straddles 10 seconds, so more runs are needed to settle it.
A support line
- ArrivalsCalls arrive
- Busiest: Agents x13Agent handles callAgents x130.6 calls waiting on average
- Supervisor handles escalationSupervisors x30.3 calls waiting on average
- DoneTime in system: 6.16 minutes95% interval: ± 0.12 minutes
Average wait at agents
17.9 seconds
95% interval: ± 3.2 seconds
Agents busy
76.5%
95% interval: ± 1.0 percentage points
Time in system
6.16 minutes
95% interval: ± 0.12 minutes
You describe it
“A warehouse with 6 pickers and 4 packers. Simulate one 8-hour shift.”
Steps in the model
PickPickers x6
Pack and inspectPackers x4
Decision tested
Add one more packer
The wait for a packer drops by 37.8 seconds (95% interval: 31.3 to 44.2 seconds).
Verdict, with 30 seconds as the smallest gain worth acting on: real and big enough. The wait falls from 42.1 seconds to about 4.3 seconds. The whole interval is above 30 seconds.
A warehouse
- ArrivalsOrders arrive
- PickPickers x60.9 orders waiting on average
- Busiest: Packers x4Pack and inspectPackers x40.5 orders waiting on average
- DoneTime in system: 13.75 minutes95% interval: ± 0.35 minutes
Average wait at packers
42.1 seconds
95% interval: ± 7.0 seconds
Packers busy
76.7%
95% interval: ± 1.4 percentage points
Time in system
13.75 minutes
95% interval: ± 0.35 minutes
You describe it
“A production line with one machining center and two finishing machines. Simulate two 8-hour shifts.”
Steps in the model
MachiningMachining center x1
FinishingFinishing machines x2
Decision tested
Add one more machining center
The wait for a machining center drops by 2.01 minutes (95% interval: 1.81 to 2.21 minutes).
Verdict, with 1 minute as the smallest gain worth acting on: real and big enough. The wait falls from 2.13 minutes to about 0.12 minutes. The whole interval is above 1 minute.
A production line
- ArrivalsParts arrive
- Busiest: Machining center x1MachiningMachining center x11.1 parts waiting on average
- FinishingFinishing machines x20.1 parts waiting on average
- DoneTime in system: 6.71 minutes95% interval: ± 0.22 minutes
Average wait at machining center
2.13 minutes
95% interval: ± 0.20 minutes
Machining center busy
74.4%
95% interval: ± 1.3 percentage points
Time in system
6.71 minutes
95% interval: ± 0.22 minutes
You describe it
“Airport security with 4 ID officers and 12 scanner lanes. Simulate 12 hours.”
Steps in the model
ID checkID officers x4
ScannerScanner lanes x12
Secondary screeningSecondary screeners x4
Decision tested
Add one more ID officer
The wait for an ID officer drops by 4.25 seconds (95% interval: 4.10 to 4.40 seconds).
Verdict, with 10 seconds as the smallest gain worth acting on: real, but too small to act on. The wait falls from 5.58 seconds to about 1.33 seconds. The whole interval is below 10 seconds.
Airport security
- ArrivalsPassengers arrive
- Busiest: ID officers x4ID checkID officers x41.0 passengers waiting on average
- ScannerScanner lanes x120.2 passengers waiting on average
- Secondary screeningSecondary screeners x40.1 passengers waiting on average
- DoneTime in system: 90.84 seconds95% interval: ± 0.37 seconds
Average wait at ID officers
5.58 seconds
95% interval: ± 0.18 seconds
ID officers busy
77.3%
95% interval: ± 0.3 percentage points
Time in system
90.84 seconds
95% interval: ± 0.37 seconds
Each example is a built-in illustrative model, run 30 times by the real engine. These are not industry benchmarks.
Why now
The AI plans. The engine computes.
Simulation shows the queues that spreadsheet averages hide. Until now it lived in desktop tools that need a specialist and weeks per study. Language models can now draft a model from a plain description, but they cannot be trusted to do the math. Stocha splits the job: the AI drafts the model and a separate engine runs it. You can read the model, rerun it and get the same numbers, and run thousands of replications without paying for thousands of AI calls.
Progress so far
928
automated engine tests pass
They include worked examples from a simulation textbook. Run on 6 October 2026.
10 of 10
basic process building blocks done
From our capability tracker: of 233 capabilities in established desktop simulation tools, 59 are done, 92 partial, 58 planned and 24 out of scope.
5
worked examples you can open
Real engine runs with 30 replications each and 95% confidence intervals.
Roadmap
- 1
Private beta
Basic process modeling, input analysis, automatic experiments, replay, plain-language results and 5 industry packs (ready-made model templates).
- 2
General availability
Conveyor and vehicle modeling, an optimizer, collaboration, data connectors, an SDK (developer toolkit) and 25 industry packs.
- 3
Enterprise scale
3D views, import from other tools, models that mix queues with feedback loops, models that update from live data and 100+ industry packs.
Pre-beta: the waitlist is open, and there are no customers or revenue yet.
How it works
What is Stocha?
Stocha is decision intelligence software that uses simulation to test business decisions before you make them. You describe the decision in plain words; an AI drafts a model of how work flows through your people, machines and space, a separate engine runs it many times, and every answer comes with a confidence interval.
Every operation has three parts
Things that are scarce
Nurses, agents, machines, rooms or lanes. There are only so many, and each serves one at a time.
Places where they wait
When every server is busy, work lines up. Queues are where you lose time, money and customers.
From a sentence to a decision
01. Describe
Say how work flows
Type how work arrives, who serves it and how long each step takes. Stocha drafts a model you can read.
arrivals → triage (triage nurse x2)
→ consultation (doctor x3)
→ done
runs: 30, report: 95% intervals02. Run
An engine does the math
The AI writes the model. A separate engine runs it many times. With the same inputs, seed and engine version, it gives the same numbers.
03. Decide
Answers as ranges
Each average comes with a 95% interval, and each comparison comes with an interval on the difference, so you can tell a real change from run-to-run variation before you act. See a decision tested.
The method
The method behind every answer
Stocha applies established operations-research practice, as taught in standard simulation textbooks. Here is what happens behind each number.
Simulation, not averages
If every arrival and every task took exactly the average time, nobody would wait, as long as there is enough capacity. Real days vary, and queues form on the bad ones. Simulation plays out those days instead of averaging them away.
Random numbers you can replay
Every run draws from seeded random number streams, so the same inputs give the same answer. When you compare two options, both face the same customers, so much of the luck cancels out and the difference reflects your decision, not which customers showed up.
Many runs, one range
Stocha repeats the day many times and reports each result as an average with a 95% confidence interval. It also tells you how many runs would shrink the range to the width you need.
Statistically real, then big enough to matter
A change counts as real only when its interval excludes zero. With several options, each interval is widened so the chance that all of them are right stays at least 95%. Then you set how big a gain must be to matter, and each worked example ends with that verdict.
Checked on every run
Every run checks that everything that arrives is accounted for. In testing, the engine reproduces standard queueing formulas and textbook worked examples. Comparing the model with your own records is a separate step, and Stocha will show you which outputs to compare.
Partly built
Inputs that fit your data
Instead of assuming a pattern, Stocha fits candidate probability distributions to your data and checks each fit with standard statistical fit tests (chi-square and Kolmogorov–Smirnov).
Today: discrete-event simulation (step-by-step queues) and spreadsheet-style static models. System dynamics (stocks and feedback loops) is partly built; agent-based models (each person or vehicle follows its own rules) are on the roadmap.
How Stocha compares
Averages hide the busy hour
A spreadsheet works with averages, but queues are driven by variability: a rush hurts more than a quiet hour helps. Simulation shows the rush. Stocha’s column describes what it is built to do.
| Feature | Stocha | Spreadsheet averages | Analysts and desktop simulation tools |
|---|---|---|---|
| What you get | An average with its 95% interval, such as 6.6 minutes (4.6 to 8.6) | One number, usually lower than the real wait, because averages hide the rush | A study report for each question |
| Who builds the model | The AI drafts it from your description and you review it | You, by hand, formula by formula | The analyst, in a desktop tool or code |
| Queues and busy hours | Built in: arrivals, waiting and limited staff or equipment | Averages hide the peak, so the wait looks smaller than it is | Handled when the analyst builds it in |
| Testing a change | Change an input and rerun the simulation | Rebuild the sheet and re-check it | Request a new study |
| Repeatable | Same inputs, seed and engine version give the same numbers | Depends on the sheet | Depends on the analyst |
| Strengths and limits | Limit: not yet proven on customer data | Strength: familiar and already on your desk | Strength: domain judgment, 3D animation depth and deep customization |
Built for
Decisions you can test, across industries
Wherever work flows through limited people, machines or space, a decision changes the outcome. Hospitals, contact centers, warehouses, factories and airports run on the same logic.
Each question opens the worked example that tests it.
Staffing
Does one more doctor cut the clinic wait?
See the exampleHiring
Does a 14th support agent cut the wait for callers?
See the exampleCapital
Does a second machining center cut the wait for parts?
See the exampleCapacity
Does one more packer cut the wait for orders?
See the exampleOne engine, many industries
44 decisions Stocha is built to test, across six groups.
Technology and financial services
Queues for compute, analysts and settlement, batches that close on a clock and rework loops in review.
- AI inference capacity
- Edge and industrial cloud capacity
Health and life sciences
Patients, samples and batches flow through scarce beds, rooms, machines and clinicians.
- Hospital bed and discharge flow
- Operating room scheduling
Consumer and lifestyle
Checkout queues, fulfillment batches with a cut-off and production lines in series.
- Subscription and meal kit fulfillment
- Smart supermarket checkout
Industrials and robotics
Fleets, vehicles and terminals share scarce equipment on fixed schedules.
- Warehouse and fulfillment robotics
- Drone and autonomous delivery operations
Energy and environment
Crews, vessels and plant windows serve repair queues and outage schedules.
- Wind and solar maintenance crew
- Grid restoration and storage dispatch
Shocks and scenarios
A demand surge, a supply outage or a new approval gate each hit a queue in a different way.
- Demand surge capacity planning
- Aging population service demand
FAQ
Frequently asked questions about Stocha
What is decision intelligence?
Decision intelligence means testing a choice before you make it. You model how the business works, try the options and compare the outcomes. Stocha does this with simulation for decisions about people, machines, space and capacity, across industries. It does not automate decisions or manage business rules.
Who is Stocha for?
Operations leaders, analysts and consultants who decide headcount, equipment or layout, and today rely on spreadsheet averages or wait weeks for a specialist study.
Why simulate instead of using spreadsheet averages?
Averages hide variability. Queues are driven by bursts, so a busy hour causes more delay than a quiet hour removes. A spreadsheet of average load can show a small wait while the real wait at the peak is long. Simulation runs many random days and reports each average with a confidence interval, so you see how sure the estimate is.
How should I read a range or confidence interval?
Each result is an average over repeated runs with a 95% confidence interval. In the clinic example, the model’s estimated average wait is 6.6 minutes and its 95% interval is 4.6 to 8.6 minutes: intervals built this way contain the model’s true average 95 times in 100. Individual patients can wait much longer than the average. Intervals on the difference between two plans show whether a change is real.
Do I need my own data?
Not to start. You describe the process and give your best estimates. Fitting distributions to your own data is partly built, using standard statistical fit tests (chi-square and Kolmogorov–Smirnov).
Does the AI do the math?
No. The AI turns your description into a model. A separate engine runs it, so the same inputs, seed and engine version give the same numbers.
Why should I trust the answers?
Each answer is averaged over many independent runs and shown with a 95% confidence interval. Two options face the same random customers, a change counts as real only when its interval excludes zero, and then it is judged on whether it is big enough to matter. On every run, the engine also checks that everything that arrived is accounted for.
Does Stocha replace analysts or simulation tools?
No. Stocha is built to give operations teams a first answer without waiting for a new study. Analysts and established desktop tools remain stronger on domain judgment, 3D animation depth and deep customization.
When does the beta open, and what will it cost?
There is no date or price yet. Join the waitlist and we will tell you when the beta opens.
Decide with confidence.
Test your next multi-million-dollar decision before you make it. Join the waitlist and we will tell you when the beta opens.