Data & AI engineering — New South Wales, Australia

Most companies don't have a data problem. They have a knowing problem.

Skopura closes the gap between the data you already hold and the decisions you actually make.

Microsoft FabricSynapsedbtPower BIAzure AI FoundryClaudeRAGAgent orchestration

What we do

Four practices, one engineering standard.

Each stands alone. Together they take an organisation from raw source systems to systems that act without being asked.

Practice 01Build

Agentic AI engineering

LLM systems that do work, not demos. Agents that read your real data, take real actions, and fail safely when they shouldn't.

  • 01 Retrieval architecture over your own corpus
  • 02 Tool-using agents wired into line-of-business systems
  • 03 Evaluation harnesses and regression suites
  • 04 Guardrails, cost ceilings, human-in-the-loop gates
Practice 02Build

Data platform engineering

The unglamorous layer everything else depends on. Warehouses, models and semantic layers that stay correct under load and under change.

  • 01 Fabric, Synapse and Databricks lakehouse builds
  • 02 dbt transformation layers with tested contracts
  • 03 Power BI semantic models and refresh optimisation
  • 04 Incremental refresh, partitioning, cost control
Practice 03Retainer

Fractional Head of Data & AI

For organisations that need senior judgement more than another contractor. Embedded leadership, hands on the keyboard when it matters.

  • 01 Data strategy and platform roadmap ownership
  • 02 Vendor and architecture decisions you can defend
  • 03 Hiring, onboarding and lifting an in-house team
  • 04 Governance that survives an audit
Practice 04Product

Productised automation

Recurring work turned into a pipeline that runs itself — built once, monitored continuously, handed over with the keys.

  • 01 Scheduled content and reporting pipelines
  • 02 Client-facing newsletters generated from live data
  • 03 CRM and operational system synchronisation
  • 04 Reporting-as-a-service with alerting on drift

How we work

Ship something real inside a fortnight.

Long discovery phases are how consultancies protect themselves.

Step 01

Find the binding constraint

One week, fixed fee. We map what you have, what it costs you, and which single bottleneck is holding the rest hostage. You get the findings whether or not you engage us further.

Step 02

Build the thinnest thing that proves it

A working vertical slice against production-shaped data — not a slide deck, not a sandbox. If the approach is wrong, you find out in week two rather than month six.

Step 03

Harden, instrument, and widen

Tests, monitoring, cost controls and failure paths go in before scope goes out. Every pipeline we ship tells you when it breaks.

Step 04

Hand over the keys

Documented, in your repos, in your tenancy, runnable by your team. We're comfortable being the firm you no longer need.

Selected work

Judged on what shipped.

Three engagements, described by what changed rather than what was promised.

Retail media

Campaign reporting that finished before the stand-up

A national retail media network whose nightly Power BI refresh had grown past three hours and was routinely failing.

Rescoped the partitions rather than the trigger method, rebuilt the semantic model, and moved campaign facts onto a daily-grain table.

3h → 10m refresh window

Publishing

A bilingual content engine that runs without an editor

A cross-border financial publisher needing daily market commentary in two languages, at a quality it could defend.

Scheduled generation pipeline with a fact-check gate, standing-facts registry, and automated publication behind a single concurrency group.

220+ articles automated

Property services

Client newsletters assembled from live market data

A property group sending weekly client updates by hand, from data that was stale before it arrived.

Warehouse-to-CRM sync, a data agent over the warehouse, and templated assembly delivered on a fixed weekly schedule as held drafts.

Weekly hands-off delivery

Fractional leadership

A head of data, at the fraction of one you actually need.

Mid-market companies rarely need a full-time data executive — they need one for two days a week, who writes code as readily as strategy. Embedded, accountable, and priced so you can end it any month.

Discuss a retainer
2 daysTypical weekly commitment
30 daysNotice period, either side
Week 1First deliverable in hand
100%Code and IP transferred

Get in touch

Tell us what isn't working.

A thirty-minute call, no deck. If we're not the right firm for it, we'll say so and point you at who is.

Or email directly —
hello@skopura.com

This form is not yet wired to a backend — see the note in the project README before going live.

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