Your data holds more than you are using. Our AI platform detects patterns, trends, and anomalies automatically and turns them into business decisions you can act on in real time.
Companies collect data from POS, CRM, and warehouses but do not know what to do with it. A controller builds a chart in Excel once a quarter, by which point it is already too late.
Invoice, warehouse, and sales data sit in five different Excel files kept on a network drive by different employees. Nobody knows which version is current. The controller stitches a table together two days before the board presentation, by which point the stock has already shipped or the customer has left for a competitor. Every decision rests on weeks-old data rather than the current state. The result: the company reacts too late or acts on outdated assumptions instead of the actual market situation and product availability.
A manager spends three hours a week exporting data from the sales system, cleaning cells, and building charts in yet another version of the workbook. They know patterns are hiding somewhere, which products sell together, which customers stop buying right before they leave, but there is never time to find them. The report is thrown together fast, shows only basic totals, and the board gets a picture that is a month behind. Decisions are made on intuition because nobody has time for real analysis, and off-the-shelf tools demand a Power BI or Tableau specialist.
We connect data from your systems, build AI models that detect patterns, and deliver results in an understandable form: a dashboard or a report.
We connect the sales system, customer base, warehouse, and invoices through REST or GraphQL APIs. Data is pulled automatically every night or in real time via webhook. We clean values, standardize date formats and product names, and fill missing records with interpolation or business rules agreed with your team. The result lands in a single PostgreSQL database or data warehouse that becomes the source of truth for the whole company. From then on dashboards always read current numbers, and manual spreadsheet stitching is no longer needed.
A machine learning model analyzes six months of sales history, identifies the buying behavior of customer groups, and predicts a drop in interest in a product two weeks before it happens. The algorithm detects unusual jumps or falls in order values, flags supply-chain problems, and suggests which SKUs to retire from the offer. Instead of scrolling thousands of rows in a sheet, management gets an email with the five most important warning signals and a recommended action. You do not need to be an analyst to understand what is going on.
We build an interactive dashboard in Metabase, Looker Studio, or Power BI where the CEO sees the last seven days of revenue, the sales manager drills into rep results, and the warehouse worker gets an alert when stock falls below a minimum. Everyone filters by region, product category, or period in one click. A PDF report generates itself overnight and lands in the board’s inbox before the morning coffee. Team members stop asking the controller for numbers because they see them live in the browser. For these rollouts we work with karolnowak.com, who handle the visual design and interface of our automations so the tools stay readable for the whole team, not just the person who configured them.
Illustrative diagram. It shows the direction of change, not specific values.
Three things a plain report does not give you: an answer in seconds instead of hours, detection of patterns nobody was looking for, and a forecast instead of a summary. Instead of asking what sold last month, you ask what will sell next week and why. The difference is the direction you are looking. In practice the report stops being a document and becomes a decision-making tool.
Faster decisions, lower operating costs, and the certainty that you are acting on facts, not guesses.
On Monday morning the board sees the exact state of warehouses, product margins, and stock turnover from Friday evening, together with trends from the last three months. No waiting a week for an analyst’s report, no guessing whether a new product sells better than an old one. A manager spots the order drop in a specific region immediately and can react with a campaign or a call to the rep the same day. The company stops operating in the dark because every decision is backed by numbers from the last few hours, or days at most.
The controller no longer spends half of Friday stitching data from five systems and formatting cells in yet another spreadsheet. The dashboard pulls data, computes metrics, and draws charts on its own. The department head does not write an email asking about last week’s sales because they can see it on screen. The team stops losing hours to manual work and starts focusing on interpreting results, talking to customers, and rolling out changes. Analytics turns from a costly chore into invisible infrastructure that runs without being reminded.
A regression model shows that if a product’s sales fell ten percent each of the last three weeks, you have an overstock problem coming in a month. A segmentation algorithm detects a group of customers who stopped ordering and can be won back with an individual offer. The system alarms when a campaign’s customer acquisition cost exceeds profitability or when delivery delays hit a seasonal business. The company reacts before the loss grows, not after every competitor has already adjusted. This is backed by McKinsey analysis pointing to automation as the main source of operational advantage.
Most often companies that have data but no decisions from it: shops with order history, manufacturers with machine data, restaurants with receipts, service firms with a customer register. The common thread is one: data is already collected but sits unused in a system nobody opens.
Manufacturing, distribution, and service companies that want to base decisions on data instead of guesses and hurriedly-made spreadsheets.
Online stores and B2B distributors need to know which products to promote and which to retire from a wide offer before the warehouse fills with dead stock. The dashboard shows the most-bought product bundles and suggests cross-selling, and it identifies regular buyers versus those who vanished after the first order. The model predicts demand for popular items in season so you avoid stockouts at the key moment of a campaign. The owner knows where to put the ad budget and which promotions give the highest return instead of shooting in the dark.
Manufacturing and logistics companies analyze raw material usage, production-line downtime, and delivery schedules to cut costs and hit deadlines. The system detects which machines fail more often than they should and when to schedule maintenance before a breakdown. The dashboard shows in real time how much stock awaits shipment, average picking times, and where bottlenecks form. The CEO knows whether the company can deliver an order without an extra shift and whether to renegotiate transport terms based on actual shipment counts. More in the sales forecasting section.
Answers to questions about integration, cost, rollout time, and your technical requirements.
We need access to the sales system, customer base, warehouse, and invoices via API or a database export. If a system has no API, we connect directly to MySQL or MSSQL over a VPN tunnel, or export data nightly via a script on your server. It is enough that the company has data in any digital form: CSV, a daily-updated Excel, or an admin-panel login. If data sits in several places, we integrate all sources into one ETL pipeline and build a common model. You do not need a data warehouse to start; we build it as part of the project.
We deliver the first working dashboard with basic metrics within two weeks of connecting to your data sources. A full system with predictive models, customer segmentation, and automated reporting takes six to ten weeks, depending on the number of integrations and the complexity of the business logic. The rollout happens in stages: first data integration and a simple dashboard, then AI models, and finally alerts and email reports. The company starts using the results after the first stage, and we add further features in the background without any downtime. More in the automated KPI reporting section.
No. We run the whole system in the cloud on our infrastructure, or in Google Cloud, AWS, or Azure on your account if you require data to stay in your environment. The dashboard runs in the browser, so your team installs no software on their computers. The only thing needed on your side is a stable VPN tunnel to the source systems, or a one-off database export if no API is available. Cloud infrastructure costs for an SMB are a few hundred dollars a month and grow only as users or refresh frequency grow.
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