Not Reporting —
Technology That Gives Direction.

Dashboards. KPIs. Reports. The familiar face of enterprise data — and the most crowded layer in the market. We looked at this layer differently. Not as an established service area — but as a process to be automated. Axoria manages the BI production process end to end with 7 AI agents. Data modeling, KPI definition, dashboard design — in hours, not weeks. We are not selling a BI project. We are selling BI production.

Demand forecasting. Churn risk. Stock need. "If I launch this campaign, how much will I sell?" Machine learning and statistical modeling — where generic models go wrong. The D-CAT approach stands on four legs: Picking the right model for the character of the data. LightGBM, XGBoost, gradient-boosting families — the industry standard for tabular data. Time series, deep learning or classical regression — whatever the problem calls for. Continuous feature enrichment and model improvement. A model is not trained once and shelved — as new data arrives, features expand, model performance is monitored and retraining is triggered. External-data integration. Weather, macroeconomic indicators, sector indices, competitive data — plugged into internal data, they multiply forecasting power. Transparent reporting of how the models work. "Why did this forecast come out this way?" is explained in language the end user can understand. Not a black box — an auditable decision foundation. And on top of all this: end-to-end data-science consulting. From dataset quality to model go-live, from MLOps to monitoring, the entire process sits in the hands of a single owner. Twenty years of domain knowledge is the engine of this technical foundation. Building a healthcare forecasting model is one thing — building it with an understanding of HBYS, the Turkish healthcare system and the doctor's daily rhythm is another. Without domain knowledge, forecasting models miss. We do not — because we know the sector as well as we know the product.