Axiomatic BI Showcase

Reliable business insights your team can own.

I help operations, finance, and growth teams replace manual reporting with automated pipelines, trusted metrics, and reporting your team can maintain after handover.

Abstract glowing pipeline core with connected data flow lanes.

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Client-owned delivery. Shared data models. Automated refreshes. Reporting and AI-ready outputs.

  • Source integration
  • Trusted metrics
  • Reporting outputs
  • Automated refresh
  • Client ownership

Why reporting breaks

Why reporting fails long before the dashboard.

Most reporting problems start upstream: manual exports, inconsistent metric logic, and delivery models that leave teams dependent on outside tools or people.

The star schema and semantic layer provides consistent metrics, lower manual overhead, and auditable reporting paths.

Manual exports and reporting.

Scheduled ingestion pipelines with explicit source contracts and repeatable runs.

Inconsistent metrics.

Shared analytical models and governed definitions so every report uses the same logic.

Poor ownership.

Client-owned code, data, automation, and handover boundaries from the start.

What I deliver

Reporting systems built for trust and handover.

I help teams connect source data, model it into a shared reporting layer, deliver reporting outputs, and automate the workflow behind them.r

Source integration

Connect APIs, exports, and file-based data into repeatable ingestion workflows built for reporting use.

Warehouse modelling

Turn raw source data into a shared reporting model with consistent definitions and reusable logic.

Reporting outputs

Build dashboards and report layers from the same trusted model so teams can work from one set of numbers.

Automation and handover

Add scheduled refreshes, documentation, and clear ownership boundaries so the system can be maintained after delivery.

Featured Example

Reference build: retail reporting pipeline

From API data to reporting-ready outputs

A working example showing how API data can be ingested, modelled into a shared reporting layer, and surfaced in reporting outputs.

The full case study includes architecture, implementation detail, and reproducibility notes for technical review.

Open Fake Store example View all examples

Fake Store sales dashboard page showing order and category level reporting.

Technology and delivery proof

Engine orchestration combines dlt ingestion and dbt transformation with client-owned delivery assets. Planned next adapters include GraphQL API and SharePoint files.

Examples to get started Fake store implementation Axiomatic BI organisation Published ADRs Code you can see