Data Analytics and AI Business Intelligence Consulting, Melbourne

Data and intelligence used to sit with IT. Now the whole business runs on it, from serving customers to growing in the market. Customer experience, competitive position and reputation all depend on it.

Jevons paradox, from economics, says that as a resource becomes more efficient to use, total consumption rises rather than falls. Steam engines became more efficient and coal use went up. Data is going the same way. Cheaper warehousing, cheaper inference and easier AI integration make the business ask for more. So size the foundation for the demand it will create, which will be larger than today's.

There's a range of reasons why your organisation might need data architecture and intelligence support:

• Data migration
• Snowflake rollout
• Data warehouse build
• New dashboards
• M&A data consolidation
• Customer 360
• AI data readiness
• Regulatory reporting overhaul
• Pre-IPO data readiness
• Master data management

Customers and the market will ask for more answers, faster, in more places. Tighter reporting and faster reaction at board level. Lineage on the day the regulator asks. APIs your commercial partners' systems can call directly. All of that takes a governed, integrated data foundation, with analytics and AI already used by the people who run the business.

We build that foundation, the analytics layer, and the AI workflows in one engagement, on platforms your team can run after we leave. One team is accountable for all three, and working artefacts come before maturity models.

Foundation: Snowflake, Databricks or BigQuery, modelled in dbt

A governed data platform built on the warehouse that fits your existing licence position, not ours. Ingestion via Fivetran, Airbyte, or Matillion. Transformation in dbt with version-controlled models and tested lineage. Master data management where entity resolution matters. Governance mapped to APP 11 of the Privacy Act 1988, the Consumer Data Right where you are a data holder or an accredited data recipient, and APRA CPS 230 if you are a regulated entity. Lineage you can show an auditor on the day they ask.

Analytics: Power BI, Tableau, or Looker on a single semantic layer

One definition each of revenue, customer and margin. Curated semantic layers in dbt or Cube feed Power BI, Tableau, or Looker so finance, sales and operations read from the same source. Forecasting, segmentation and propensity models built on Snowflake Cortex, Databricks ML, or BigQuery ML.

Intelligence: Claude, GPT, or Gemini behind human-in-the-loop gates

AI workflows designed around the decisions your people make. Claude, GPT, or Gemini for reasoning. Human approval gates on every decision with a dollar value or a customer name attached. Evals run before deployment. Claude Code, Cursor, n8n, Make, or Zapier MCP for orchestration, picked on what your team can maintain after we leave.

Answer a customer's question the same day. Produce the board report from one source rather than reconciling three. Hand the auditor what they ask for on the spot. Take up a market opportunity without a six-month integration first. Run the AI initiative your CEO wants on a foundation that can hold it.

We recommend a platform on what your workloads cost to run and what your team can operate, not on a reseller margin. The first phase targets a single workflow in production rather than a roadmap to one. Your team operates the artefacts on day one; if you want us back for the next layer, you call us.