Insights
Product, design, AI, and engineering perspectives from our team.
Feature Stores for Machine Learning at Scale
Growing data teams often outgrow spreadsheets and ad hoc pipelines long before they need a full feature store platform. Here's how to tell the difference — and how Google Cloud's Vertex AI implements feature management as a concrete example.
AI Services & Data Sovereignty for Regulated Australian Industries
A practical guide for fintech, healthtech, and insurance leaders on where AI training and inference data can legally live under the Privacy Act 1988 and APRA CPS 234 — plus architecture patterns for keeping sensitive data onshore while using cloud AI services.
Engineering Leadership Succession Planning During a CTO Exit
A CTO exit puts delivery velocity, architectural continuity, and team retention at risk if handled as a recruitment problem alone. Here's how to structure interim leadership, preserve institutional knowledge, and protect momentum during the transition.
CLV Modelling for SaaS and E-commerce Growth Teams
A practical guide for SaaS and e-commerce data leaders on building customer lifetime value models that inform retention and acquisition spend. Covers data requirements, modelling approaches, and common pitfalls for teams scaling past product-market fit.
Causal Inference for Business: Beyond Correlation in Analytics
Correlation-based dashboards can tell you what happened, but not what will happen if you change something. This article explains when that gap misleads resourcing and pricing decisions, and how A/B testing, quasi-experiments and uplift modelling give growing Australian companies more reliable answers.
Data Lineage and Cataloging: Trustworthy, Discoverable Data
Data lineage and cataloging are the unglamorous foundations that make AI and self-serve analytics trustworthy. This guide covers what to build, in what order, and how the Australian Privacy Principles shape the work.
Quantifying Technical Debt: A Business Case Your Board Approves
Boards fund reduced risk, protected revenue, and faster time to market — not "code quality." This article shows how to translate technical debt into cost of delay, risk exposure, and opportunity cost, with a board-ready register template.
SRE for Scale-Ups: Reducing Incidents Without a Team
Reliability incidents rising as you scale don't require a dedicated SRE team to fix — they require the right practices. Here's how CTOs can adopt error budgets, incident response, and on-call rotations with existing engineers.
Board-Level AI Literacy: A Director's Guide
Board-level AI literacy doesn't require directors to understand machine learning — it requires knowing what questions to ask. This guide sets out a practical framework for AI oversight grounded in existing directors' duties.