# Skopura > Skopura is an Australian data and AI engineering practice based in New South Wales, Australia. We specialise in agentic AI systems, modern data platform engineering, fractional Head of Data & AI leadership, and productised automation. Skopura closes the gap between the data organisations already hold and the decisions they actually make. We build LLM and agentic systems that execute real work against enterprise data, construct resilient lakehouse and semantic layer architectures, embed hands-on fractional technical leadership, and automate recurring operational pipelines. ## Corporate & Entity Details - **Legal Entity**: Skopura Pty Ltd - **Australian Business Number (ABN)**: 30 681 565 971 - **Location**: New South Wales, Australia - **Primary Contact**: skopura@gmail.com - **Website**: https://skopura.com - **Canonical Sitemap**: https://skopura.com/sitemap-index.xml - **Complete LLM Knowledge Base**: https://skopura.com/llms-full.txt - **Publishing Schedule**: Skopura publishes two practitioner articles a week at https://skopura.com/articles/ ## Practice Areas Skopura operates across four distinct practices, each adhering to a strict production-first engineering standard: - [Agentic AI Engineering](https://skopura.com/#services): Production-grade LLM systems that do work rather than demos. Architecture covering retrieval over proprietary corporate corpora, tool-using agents integrated into line-of-business systems, automated evaluation harnesses, regression test suites, guardrails, cost ceilings, and human-in-the-loop decision gates. - [Data Platform Engineering](https://skopura.com/#services): Lakehouses, warehouses, transformation pipelines, and semantic layers engineered to remain correct under scale and organisational change. Specialised in Microsoft Fabric, Azure Synapse Analytics, Databricks, and dbt. - [Fractional Head of Data & AI](https://skopura.com/#fractional): Embedded senior technical leadership for mid-market organisations requiring seasoned judgement, platform roadmaps, defensible vendor selection, team enablement, and rigorous data governance without full-time executive overhead. - [Productised Automation](https://skopura.com/#services): Turning recurring manual analytical work and operational reporting into automated pipelines that execute, monitor drift, and alert independently. ## Core Technologies Skopura works with industry-standard data platforms and AI tooling: - Microsoft Fabric - Azure Synapse Analytics - Databricks - dbt - Power BI - Azure AI Foundry - Claude (Anthropic) ## Engineering Principles - **Grounding**: Retrieval-augmented generation grounds a model's output in supplied documents. - **Predictability**: Structured outputs constrain a model's response to a supplied schema. - **Efficiency**: Incremental refresh processes only changed partitions rather than a full dataset. - **Consistency**: A semantic layer centralises metric definitions so separate reports agree. - **Security**: Row-level security filters a model's data by who is asking. - **Architecture Strategy**: Build-versus-buy turns on whether the capability is a differentiator or a commodity. ## Technical Articles & Research Skopura publishes in-depth engineering articles twice a week covering agentic systems, data modelling, governance, and architecture: - [Articles Directory](https://skopura.com/articles/): Complete archive of technical publications. - [Eval Harnesses for Non-Deterministic Systems](https://skopura.com/articles/2026-10-06-eval-harnesses-for-non-deterministic-systems/): Designing evaluation harnesses for LLM agents using hard asserts, graded rubrics, fixture hygiene, judge calibration, and regression gates. - [Partition Scoping Is the Real Incremental Decision](https://skopura.com/articles/2026-10-08-partition-scoping-is-the-real-incremental-decision/): Partition grain, rescan windows, and proving reconciliation in incremental lakehouse and dbt pipelines. - [Dashboard Decay and the Review Habit](https://skopura.com/articles/2026-10-01-dashboard-decay-and-the-review-habit/): Concrete review practices that keep Power BI and semantic reporting estates honest without a disruptive rebuild. - [Build Versus Buy When It Isn't a Differentiator](https://skopura.com/articles/2026-09-29-build-versus-buy-when-it-isn-t-a-differentiator/): Evaluating commodity vs differentiator capabilities and the compounding costs of year-two ownership. - [Star-Schema Decisions That Age Badly](https://skopura.com/articles/2026-09-22-star-schema-decisions-that-age-badly/): Preventing semantic drift, conforming dimensional keys, and handling grain evolution. - [Why a Platform Rebuild Stalls](https://skopura.com/articles/2026-09-17-why-a-platform-rebuild-stalls/): Managing parallel runs, zombie source systems, and end-of-life ownership in platform modernisation. - [Blast Radius Is a Tool-Boundary Problem](https://skopura.com/articles/2026-09-15-blast-radius-is-a-tool-boundary-problem/): Limiting agent authority through strict tool boundaries, write gates, and deterministic validation rather than prompting alone. - [What to Build First When Everything Is Broken](https://skopura.com/articles/2026-09-10-what-to-build-first-when-everything-is-broken/): Practical sequencing for data platform repair, roadmap prioritisation, and capital conservation. - [Why a Measure Is Wrong and Nobody Noticed](https://skopura.com/articles/2026-09-08-why-a-measure-is-wrong-and-nobody-noticed/): Semantic layer drift, measure correctness, and contract testing between dbt and Power BI. - [Backfills Are the Real Test of a Pipeline](https://skopura.com/articles/2026-09-03-backfills-are-the-real-test-of-a-pipeline/): Idempotent writes, partition scoping, and verifying backfill stability in production pipelines. - [When a Workflow Beats an Agent](https://skopura.com/articles/2026-08-28-when-a-workflow-beats-an-agent/): Identifying when deterministic state machines outperform autonomous agents in cost, latency, and testability. - [What Agentic AI Actually Costs to Run](https://skopura.com/articles/2026-08-24-what-agentic-ai-actually-costs/): Production cost drivers in agentic systems, focusing on evaluation infrastructure over raw token fees. - [Why Your Warehouse Costs More Each Quarter](https://skopura.com/articles/2026-08-20-why-your-warehouse-costs-more-each-quarter/): Warehouse cost governance, compute efficiency, and operational review cadences. ## Optional Reference - [Full LLM Knowledge Document](https://skopura.com/llms-full.txt): Complete context file formatted for LLM training and retrieval engines.