Mathematical and statistical models for decisions that are costly to get wrong.
Grabatus builds forecasting, optimization and experimentation models for companies that decide under uncertainty: how much to buy, how many people to schedule, what price to charge. You decide knowing the risk of each option.
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You make decisions every day without seeing the whole picture.
- How much to buy for next month.
- How many people to schedule on Saturday.
- Which price protects the margin without losing sales.
Each of these choices has a cost when it goes wrong. And the data you have shows only part of what can happen.
You can’t test every option.
Price, inventory, route, shift, product mix: the combinations never end. Statistical models tell you where it is worth measuring and how much is still unknown.
Statistics measures uncertainty. Optimization chooses the best path within it.
Demand forecasting
How much will I sell next month?
A good forecast doesn’t deliver a single number. It delivers the range where sales should land and the chance of each scenario. That is what you plan purchasing, production and cash with.
Inventory
How much to buy, when running out costs more than overstock?
Overstock shows up in the inventory. Shortage only shows up as lost sales. When running out costs 4 times more than overstock, ordering at the right point of the distribution, not at the average, cuts about 30% of the expected cost.
Staffing and operations
How many people, on which shift, in what order?
Shift schedules, delivery routes, production sequences: thousands of combinations and real labor rules. Optimization finds the one that costs least and meets every rule.
A/B tests
Did it work or was it luck?
You changed the price, the display or the sales script, and the number went up. A well-designed test shows whether it was the change or chance, before you roll it out to the whole operation.
Managing means closing the gap between result and target.
Indicator, target and action plan. Every good management model comes down to two questions, and both have a mathematical answer.
- Which plan reaches the target at the lowest cost?
- Optimization answers.
- Did the result change because of the plan or by chance?
- Statistics answers.
Method
Each measurement is chosen to teach the most.
The model estimates the result of each option and the size of its own doubt. The next measurement goes where the expected improvement is greatest, which is why the best point appears in few attempts.
- measurements
- 2
- best
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- true peak
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Two points measured. The model already has a guess and knows where it is still unsure.
How we work
Start small. Grow with results.
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01
Diagnostic
We understand the decision, the available data and what a mistake costs. You get a map of what is worth modeling and the expected return.
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02
Pilot
A model running in a real part of the operation, compared with how it is done today. The numbers say whether to keep going.
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03
In production
The model goes where the decision happens: in the ERP, in the spreadsheet or via API. And it keeps being measured.
Platform
Ready-made analyses, straight from your CSV.
Demand forecasting, A/B testing, basket analysis and survival analysis as a service: send a CSV through the browser, the API or MCP and get a result ready for deciding.
- Demand forecasting
- A/B Test
- Basket analysis
- Survival analysis
Which decision is costing your company money?
Describe the problem in a few lines. We reply with what can be modeled, the data needed and a right-sized first step.
We serve manufacturing, logistics, retail and B2B services.
Why use mathematical and statistical models in business decisions
Mathematical and statistical models turn a company’s history into probabilities and plans. Instead of deciding by the average or by intuition, you see the risk of each option, choose the one with the lowest cost and measure whether it worked.
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Smaller forecast errors
Forecasts with an uncertainty range show the likely scenario and the worst case. Purchasing, production and cash stop being planned around a single number.
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Inventory at the right level
The model weighs the cost of running out against the cost of overstock and tells you how much to order. Fewer stockouts and less capital sitting on the shelf.
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Cheaper operations, without breaking rules
Schedules, routes and production sequences are chosen from thousands of combinations, respecting working hours, capacity and deadlines.
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Changes proven before scaling
A/B tests and experiments separate real effects from luck. You only roll out to the whole operation what truly worked.
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Decisions that explain themselves
Every recommendation comes with the assumptions, the data used and the size of the uncertainty. It can be discussed, audited and corrected.
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Predictability for planning
With risk measured, targets, budgets and service levels are agreed on numbers, not hope.
What are mathematical and statistical models for business?
They are representations of operational data that estimate what is likely to happen and how likely it is. With them, a company forecasts demand, works out how much to buy, builds staff schedules and tests changes while measuring the risk of each decision.
Which business problems does statistical modeling solve?
Sales and demand forecasting, inventory sizing, staff scheduling, routing, pricing, market basket analysis, customer retention and A/B test evaluation. What they have in common: repeated decisions where mistakes cost money.
Do I need a lot of data to get started?
No. Spreadsheets with your sales, order or service history are usually enough for a first model. In the diagnostic we assess what exists and what can be done with that data.
What is the difference between statistics and optimization?
Statistics measures what is unknown: how much you will sell, the chance of running out, whether an effect is real. Optimization uses those measurements to choose the best plan among all possible options, while respecting the rules of the operation.
How does Grabatus start a project?
With a short diagnostic of the decision, the data and the cost of getting it wrong. Then comes a pilot in a real part of the operation, compared with the current process. Only then does the model go into production, integrated with your ERP, your spreadsheet or via API.
Is this artificial intelligence?
It uses the same mathematical foundation, with a different focus: explainable models, built for a specific decision and measured against the result. You know why the model recommends each number.
Can I use it without commissioning a project?
Yes. On the Grabatus platform you create an account, upload a CSV and run ready-made analyses such as demand forecasting and A/B testing.