Agentic ETL · DWH · BI · Visualization — An agentic workbench that runs the corporate data warehouse build end to end. 7 AI agents, human-in-the-loop architecture, up to 85% speed-up.
Consultants go into source systems. Which tables exist, what each column means, how good the data quality is — they review them one by one.
Fact or dimension? Which KPI is computed how? Which ETL job is written next? Every decision takes human effort.
Semantic models and measures are coded. Each step builds on the previous one. Every step carries error risk.
Not an automation tool — a thinking, deciding, learning colleague.
Each agent feeds on the previous one's output. At every handoff, you step in — Human-in-the-Loop.
Runs on-premise. No real customer data ever leaves. Detects PII in the source database (national ID, IBAN, name, email) and transforms it while preserving format. The KVKK compliance report is generated automatically.
It does not throw random queries — it explores the database step by step. Descends into suspicious areas and investigates the links. Output: automatic data dictionary + KPI suggestions. A draft that used to take consultants weeks — now in minutes.
Every table and every column is reviewed systematically. A 0–100 quality score. High-risk fields in red, fields needing attention in yellow. Approved quality rules are added automatically to the ETL process and the test infrastructure.
Fact / dimension choices, granularity, SCD types — all analyzed and presented with rationale. Output: visual model + SQL CREATE TABLE statements. Automatic DDL from the approved model.
Produces the visual design of dashboard wireframes before the DWH is even built. Aligns business units' expectations. The "that's not what we meant" tensions disappear.
Produces output formats aligned with modern data-engineering standards. Instead of writing from scratch, review the generated code and adapt it.
Measures, time comparisons, growth rates — generated automatically, with explanations. Output: a ready-made semantic-model package. RLS rules are carried over automatically.
On-premise deployment isn't a marketing preference.
The Axoria Privacy Agent runs as agent zero. All the AI sees is synthetic data — carrying no real identity information but preserving statistical properties.
Concrete outputs are produced for KVKK compliance.
Every AI call is logged. Which agent · which model · how many tokens · what cost — tracked in real time.
Typical mid-sized project: 1–2 USD total AI cost.
Doing the same process manually: several consultants for several weeks.
Axoria sits at the center of the D-CAT Technologies product family. The HealthCat healthcare vertical is built on top of Axoria. Common infrastructure can be shared with Tracta, Orix and Promotion Intelligence.
7-agent architecture, human-in-the-loop, on-premise security. Measurable, predictable, repeatable.