Gambling or High level decision?
Take a huge step forward with our extreme mathematics
Gambling or High level decision?
Take a huge step forward with our extreme mathematics
How much did the big grocery shopping trip three weeks ago cost? Memory is an important part of decision-making mechanisms. You need to remember all the coefficients involved in a decision during the decision-making process. It's already difficult for us to keep even 6 two-digit numbers in our heads in a row.
For example, look at the following sequence of numbers and try to repeat it:
92 49 61 57 23 84
In a business process, there can be thousands of numbers. Moreover, their combined effect must also be taken into account, which explodes exponentially. In contrast, people usually don't even remember what they had for lunch 2 weeks ago.
Human memory is limited, but computer memory is perfect. It instantly retrieves the photo of the food or the transactions.
How can we expect a high level of decision-making from ourselves as humans? If your memories are so vague, how do you make accurate decisions?
Even trying out all permutations of just 20 things – which is the same as figuring out how many different ways 20 people can sit down around a table – at a rate of one permutation per second, would take you 77 billion years. This is a simple mathematical problem that humans cannot process because it requires linear thinking, but in real life, the problem is much more complex than simple sequentiality. Humans are slow, computers are fast – and they don't get tired.
If you decide faster, you earn more money – but not alone, but with the help of technology.
The sales statistics of a webshop's products are a huge flood of data; a human gets lost in it, but a computer filters out the essentials in moments. The profit generated by a given product and how to consider it based on market trends, product lifecycle, as well as customer needs and fresh data, represents a massive database. Humans are unable to recognize all the hidden relationships and interactions between each number and factor. Humans also cannot distinguish between useful and useless information.
Mathematical models can handle big data, take into account the relationships between different factors, and adapt to new data in real-time, all while the human doesn't have to struggle with the complexity. A model-based approach is the only way to make optimized and reliable decisions. Only mathematical models create value from noise.
Customer price sensitivity is unpredictable and often contradictory. You can offer the same product at multiple prices. You can expect different reactions and unpredictable decisions from individual customers.
Uncertainty cannot be managed with intuition, but with mathematical and statistical models.
In rapidly changing markets, there's no magic formula, only rapid adaptation. The prices appearing in the market are characterized by constant fluctuation. The same will be true for your products. You need a model that adapts automatically and quickly.
How do we price the product now to maximize our expected profit in the long term? We're only talking about 5 sales events for one product. Let's consider the time-based trends, the expected number of sales and its relationship to the chosen price, and even our profit margin.
A human guesses, a machine models and shows the one that yields the highest profit. The human mind is unable to process and evaluate large amounts of complex data with the necessary accuracy. Decision-making may be based not on mathematical or logical analysis, but on an emotional and subjective approach, which does not guarantee the optimal outcome.
Determining which product is the most important is not a simple question. The complex interplay of profit, sales volume, market freshness, and other factors creates extremely high complexity, and the human decision-maker cannot process all of this simultaneously without overlooking certain relationships or making mistakes.
Intuition only works when you don't have a better tool.
The question isn't how much data you have, but what you do with it. Most businesses know too little and yet try to make decisions based on gut feelings. We, on the other hand, can make accurate decisions, even from a small dataset.
Do you know how many sales Amazon needed to set the perfect price? And how many customers do you have?
1 data point: human tries to infer – guessing, shrugging hands.
100 data points: already a graph, but the human is still uncertain.
1000 data points: too much data – the human gets lost.
The computer, however, extracts value from all three using mathematical and statistical models.
What data is actually available? Selling price? Purchase time? Product characteristics? Customer type?
Based on this, do you make a decision? Maybe, or perhaps next month when there's enough data?
The computer makes the decision, even multiple times a day.
Do you really follow the trends? We show changing demand (e.g., from Monday to Friday) that humans don't even notice.
Meanwhile, the machine has already adjusted prices, reacted, and learned.
You don't need a lot of data – you just need enough for a good decision. The difference is that we know how.
Monitoring competitor prices doesn't create a competitive advantage – it's just following. If everyone does the same thing, no one wins. The one who optimizes gets ahead. As a business manager, you also check competitor prices daily.
But do you know that they are doing the same with you?
Companies watch each other – like a hall of mirrors, everyone is spying on everyone else.
How do you know if a competitor's price is actually optimized? They might have made a wrong decision themselves.
Why do you assume that based on their data, the price will be good for your product?
If a competitor sets a bad price – everyone copies it, meaning everyone loses.
Can you cover the entire market with a single price observation? → No.
Can you determine the optimal price purely from external data? → No.
What is the goal: to copy or to win? → To optimize, to maximize profit.
The copying strategy is reactive, slow, and blind.
The optimization strategy is proactive, fast, and data-driven.
While one company follows the other and stagnates, the other uses its own model, and its profit curve rises.
If everyone walks the same path, no one goes far. The one who optimizes overtakes the line – doesn't follow it.
Machine learning (ML) is magical. If you have millions of data points. If there's no noise. If everything repeats.
But price optimization isn't like that.
You don't have millions of data points – for one product, you might only have 50 sales per year. And even this small amount of data is full of randomness: weather, random events, campaign effects.
The machine learns – but it doesn't know what it's learning. It sees noise as signal. And it learns that. The ML algorithm in the "learning" phase – connects rain with sneakers, holidays with chairs.
What it outputs is not a decision. Not a forecast. Just a guess. A random number in a suit. It's as if an AI robot is spinning a roulette wheel as a "price suggestion."
We don't start with machine learning. We build a mathematical model behind the decision – where we infer even from little data and separate noise from signal. That is, it separates the structured pattern from random effects.
If the machine doesn't know what it's being taught – then it's not making a decision, it's just rolling dice.
Forecasting can be misleading – what matters isn't what's expected, but what's happening right now. The machine monitors purchases in real-time and intelligently changes the price based on this to achieve maximum profit.
They predict rain → surely many people will buy waterproof boots → we raise the price, but no one buys boots in the store.
A few days later, in sunshine: demand suddenly increases – but now the boots are cheaper.
Consequence: loss of profit.
What's the problem with this? Customers don't react when we think they will. Weather is an external signal, but purchasing is an internal pattern. Raising the price too early can scare away customers or maximize at the wrong time.
We don't infer in advance → we don't speculate. We only monitor actual purchasing activity.
If demand increases → we raise the price at that moment. Someone buys a pair of boots → the price immediately increases in the system → optimized profit.
Why is this strategy better? Adaptive → it reacts not to plans, but to actual behavior. It maximizes profit without delay. It doesn't predict! – It DECIDES!
We can't influence the weather. But we can influence how much we earn on a pair of shoes when it starts to rain.
Demand is not the same as sales. We can't react to what we don't see. That's why we don't chase demand – instead, we react to real-time sales, not noisy, vague signals.
You might think mistakenly that what people visit or place in their cart is demand. But what if they were just interested... and moved on? For example, there are many visitors on a product page, but no one buys. Yet, the system sees: "something happened". Demand is not equal to sales. Demand is interest. Sales are the decision. People click, browse, compare – and then leave. And the system often interprets this as: demand. But in reality, we don't see demand – we can only guess at it.
Website traffic is full of noise:
Automated bots
Curious onlookers
Price comparers
If we react to these with new prices, we can achieve incorrect price movements, an unstable system, customer uncertainty, and loss. Prices jump up and down nonsensically → customers look confused → fewer conversions.
A sale is a real signal. Someone actually bought it. Therefore, we only react to this – we don't speculate. This avoids overthinking, avoids false reactions, and results in stable, logical decisions.
Demand is just a whisper in the dark. Sales are the light switch. We only act when someone turns it on.
A/B testing is like trying to choose between two colors... on a dead-end street. The world isn't A or B, but a system of interconnected decisions that requires fast, intelligent models – not experimentation.
What happens if you try out two prices? Can you tell which one worked better... three weeks later? Someone is running an A/B test while the world changes around them – competitors change prices, trends transform, time passes.
Slow – it takes time, while the market doesn't wait.
Simplified – it only chooses from two options.
Irrelevant in a changing world – what you gain today, you lose tomorrow. A/B testing pretends the market is static. But it's not. Reality is noisy, dynamic, and competitive.
What do we use instead? Real-time, adaptive models. Multivariate, context-aware decision-making
The system doesn't choose from two options, but constantly learns and adapts. The dynamic pricing system monitors sales, automatically reacts, changes, tests, learns – all without human intervention.
A/B testing is a question. We already know the answer – because we've already decided.
A new trend is starting. Your products are selling out. Customers are looking for them. And you don't even know it yet. You're looking at an Excel spreadsheet while demand is rising and prices remain unchanged. By the time you notice something is happening – it's over. The profit you could have made has slipped away. Then the trend reverses. You raise prices when no one wants to buy anymore. This is manual pricing. Slow, blind, retroactive.
The mathematical model works differently: it reacts in real-time, while you would still be asking questions. Prices adjust automatically, maintaining a stable profit line even amidst market noise. The model doesn't think, doesn't hesitate. It just calculates – and brings in the money.
Manual decisions? Slow profit. Mathematical model? Maximum profit. Choose.
The market is not linear. Not simple. Not stable. A tiny change – a huge consequence. This is chaos. And a template algorithm won't help you in this.
A butterfly flaps its wings in Asia – and a hurricane breaks out in America.
The market environment is similar: sensitive, dynamic, unpredictable.
What does an off-the-shelf algorithm do? You download an algorithm. You upload the data. You think it works. But these models look at the data globally. They average, equalize, smooth. The trend? The local spike? The time? It disappears from the equation.
If someone bought 10 shoes yesterday, that's different from if they bought them today. But the algorithm doesn't know this. It doesn't care when it happened.
A model that observes locally, is sensitive to change, and treats time as a real dimension. It doesn't average. It reacts. It lives. A real-time system that constantly adapts to fresh sales data.
There is chaos behind the data. If you blindly smooth it, the essence is lost. You don't need an algorithm. You need a decision-making system that knows when time matters – and when to act.
In theory, everyone can use AI. In practice? APIs, parameters, training, validation, hyperparameter tuning, fine-tuning… The business manager's face freezes when they see messages like "Tensor shape mismatch," "Model convergence failed," "Fine-tuning in progress" on the screen. You don't get a system. Just another problem.
Bulk editors? Manual work. AI apps? Complex setup. You don't understand it – and you shouldn't have to. When you manually set prices in a Shopify app vs. trying to "automate" something → nothing meaningful happens.
We don't give you an engine, we give you a car. We give you decisions. No configuration needed. No training required. You just install it – and the model is already working. With two clicks, you activate the system, and in the background, the algorithm starts automatically, works on the data, and modifies prices.
Simplicity + Efficiency: We hide the complexity.
The mathematics works in the background. In the front, you only see the result: better prices, more profit, less stress.
You understand your business. We understand decision-making. Don't waste your time training AIs – when you could have a ready-made model working for you on your team.
When it comes to business, we tend to take chances. Why? Because we humans like to play with luck. But what happens when luck runs out?
Risk promises big profits. But what happens if the stakes are too high? A decision you make emotionally can easily lead to loss. For example, you make a big decision (e.g., a price increase), and customers walk away from your business, profits decrease.
We don't gamble with luck. Mathematical models only analyze the data. No feelings, no risk – just facts. Therefore, their decisions are more reliable.
What do you choose? Risk? Chance? Or are you sure that your decisions are based on the best data?
Risk is always present. But we can reduce it. We don't gamble with luck. We ensure your success. Don't let luck decide for you. Let mathematics guide you.