Agentic ETL · DWH · BI · Visualization — an agentic workbench that runs enterprise data warehouse delivery end to end. 7 AI agents, human-in-the-loop architecture, up to 85% acceleration.
Axoria · live control panel — workspace, agent, cost and data-quality on a single screen.
Consultants log into source systems. Which tables exist, what each column means, how clean the data is — all of it inspected one by one.
Fact or dimension? How is each KPI calculated? Which ETL jobs are written? Every decision is human effort.
Semantic models and measures are coded. Each step builds on the one before. Every step carries error risk.
Not an automation tool — a thinking, deciding, learning teammate.
Each agent feeds on the output of the previous one. At every handoff you step in — Human-in-the-Loop.
Runs on-premise. No real customer data leaves the perimeter. Detects PII (national ID, IBAN, name, e-mail) in the source database and transforms it while preserving format. A KVKK compliance report is generated automatically.
Doesn't run random queries — it explores the database step by step. Drills into suspicious fields, investigates relationships. Output: automatic data dictionary + KPI suggestions. A draft consultants prepare over weeks — in minutes.
Every table, every column is examined systematically. A 0–100 quality score. High-risk fields red, careful-watch fields yellow. Approved quality rules are added automatically to ETL and the test framework.
Fact / dimension calls, granularity, SCD types — analyzed and presented with rationale. Output: visual model + SQL CREATE TABLE statements. Automatic DDL from the approved model.
Generates the visual design of dashboard wireframes before the DWH is even built. Aligns business-unit expectations. "We didn't mean that" tension disappears.
Produces output in formats aligned with modern data-engineering standards. Instead of writing from scratch, review and adapt the generated code.
Measures, time comparisons, growth rates — generated automatically, with descriptions. Output: a ready semantic-model package. RLS rules are propagated automatically.
Architect builds the data model, Guard measures quality, Pulse delivers the semantic model. Live panels below.
Fact / dimension calls, granularity, SCD types — Architect explains with rationale. Approved models produce automatic DDL; schemas are versioned.
Every table, every column. 0–100 score, red/yellow/green risk map. Approved quality rules are passed automatically into ETL and the test framework.
Measures, time comparisons, RLS — generated automatically, with descriptions. Delivered as a ready package.
On-premise deployment is not a marketing choice.
The Axoria Privacy Agent runs as agent zero. All the AI ever sees: synthetic data that carries no real identity but preserves the statistical properties.
Concrete artefacts are produced for KVKK / GDPR compliance.
Every AI call is logged. Which agent · which model · how many tokens · what cost — monitored in real time.
Typical mid-size project: 1–2 USD total AI cost.
Same process done manually: several consultants over several weeks.
Axoria sits at the heart of the D-CAT Technologies product family. The HealthCat healthcare vertical is built on Axoria. Tracta, Orix and Promotion Intelligence share common infrastructure.
Not just producing data — an autonomous analyst layer that monitors continuously, catches anomalies and generates briefs. Data Studio prepares the data; Analytics Agents interprets it and recommends action.
7 agents, 1 panel, real-time cost and quality metrics.
7-agent architecture, human-in-the-loop, on-premise security. Measurable, predictable, repeatable.