Insights
Product, design, AI, and engineering perspectives from our team.
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.
Data Mesh vs Centralised Data Platforms for Mid-Market Growth
Choosing between data mesh and a centralised data platform is an organisational decision as much as a technical one. This guide walks Australian scale-up leaders through team structure, governance overhead, and when each pattern fits.
AI for Insurance and Insurtech: A Practical Guide
A practical look at where AI genuinely fits in insurance and insurtech — underwriting support, claims triage, and fraud detection — and the data quality and explainability obligations Australian insurers need to get right first.
Building an Experimentation Platform for Product Teams
Moving from ad hoc A/B tests to a real experimentation platform means getting event tracking, statistical rigour, and feature-flagging right — together. Here's what that infrastructure actually requires.
Building an A/B Testing Platform for Product Teams
An experimentation platform stands on three pillars — event tracking, statistical rigour, and feature-flagging infrastructure. This article breaks down why ad hoc A/B testing breaks at scale and how to decide whether to build, buy, or combine.
FinOps for Engineering Leaders: Controlling Cloud Spend
Cloud spend rarely spikes overnight — it creeps until the AWS or Azure bill becomes a board-level issue. Here's a practical framework for Australian engineering leaders to structure account governance, attribute spend, and decide between SaaS tooling and embedded support.
Platform Engineering for Scale-Ups Without a Dedicated Team
Growing engineering teams don't need a dedicated platform team to get the benefits of platform engineering. This guide covers internal developer platform (IDP) patterns and golden paths that reduce cognitive load without a big hiring investment.
LLM Cost Optimisation: Cutting Spend Without Cutting Quality
Cutting LLM spend doesn't have to mean cutting quality. This guide covers the practical engineering levers — model tiering, prompt caching, batching, prompt budgets, and observability — with a worked example showing how they compound.
Data Science Consulting in Melbourne: A Scoping Guide
Scoping your first data science engagement well matters more than picking the right vendor. This guide covers problem framing, data readiness checks, deliverable shapes, and pricing models for Melbourne-based mid-market companies.