Enterprise data creates value in five layers: what happened, why it happened, what could happen, which option to choose, and at the top, a perceptive layer that monitors them all continuously. For each layer we show clearly what we deliver today and what is on the roadmap.
The four layers build on one another; the fifth layer at the top monitors them all continuously and feeds findings, with their evidence, back to every layer.
What are we missing?
A team of analysts working above your dashboards: it monitors the four layers continuously, produces findings without being asked and links every figure to its source. It shows what changed and where it is concentrated; it does not answer the question of why, and interpretation and decisions stay with people.
AgenticObjects product · Implemented by D-CAT
The Autonomous analyst runs on a schedule and records deviations with their evidence.
The Conversational analyst answers questions together with the evidence behind them.
Findings, Recommendations and Briefs are never deleted; they are archived and remain traceable.
A deterministic engine produces every number and verifies it before publication.
Dashboards, KPIs and reports: the familiar face of enterprise data. The data warehouse and semantic model are the foundation of this layer.
What we deliver today
Shows which channel, segment or campaign a deviation is concentrated in and analyzes possible drivers. Diagnostic analysis with machine learning is set up per project under BI-Driven AI; interpretation and decisions stay with the business team.
What we deliver today
Models questions such as demand, risk and volume using historical data and external signals. Forecasting models are set up per project under BI-Driven AI.
In our products: on the roadmap
Combines the forecast with business rules and targets, and compares options through what-if simulation and optimization. The system recommends; the decision stays with people.
In our products: on the roadmap
What we do in each layer, and how.
Data modeling, KPI definitions and dashboard design are the work of this layer. Axoria Data Studio runs this production process with AI agents, with human approval at every step.
Not “sales went down”, but “this campaign, in this channel, in this segment did not deliver the expected result.” Drill-down reports show the breakdown of a deviation; statistical analysis ranks the possible drivers.
Demand forecasting, churn risk, inventory needs: “If I launch this campaign, what will I sell?” Our approach rests on four pillars.
Industry knowledge is the engine of this setup: in healthcare, knowing the hospital information system (HIS) and how a hospital runs makes sure the model asks the right question.
The forecast is combined with business rules, past decisions and an optimization target, and options are compared through what-if simulation. The system recommends; evaluation and decisions stay with people.
Prescriptive analytics for Promotion Intelligence and HealthCat is on the roadmap; today we deliver it on a project basis.
Four questions clarify your current layer and the next step.
If the same KPI shows different values in different reports, the foundation of the descriptive layer needs strengthening first.
If you cannot drill down by channel, segment or campaign, the diagnostic layer needs drill-down and a data model.
If forecasts are made by hand in spreadsheets, the predictive layer needs historical data and features.
If what-if scenarios and optimization targets are defined, the ground is ready for the prescriptive layer.
The 2-week Health Check reviews your existing BI setup: a health scan, an opportunity map and KPI alignment. The result is a concrete report that shows your current layer and the next step.
In a 30-minute assessment, let's discuss your current layer and the next step.
