Insights
Product, design, AI, and engineering perspectives from our team.
Healthtech Interoperability Standards for AI in Australia
A practical guide for technical leaders building AI features on Australian clinical data — covering FHIR, My Health Record integration, ADHA standards, and where TGA regulation applies before deployment.
AI Vendor Risk: Contracts, SLAs & Liability for AI Features
Embedding third-party AI into your product introduces risks that standard SaaS contracts and SLAs were never designed to cover. Here's what technology and business leaders need to check before they sign.
AI-Augmented Test Automation for Legacy Modernisation
AI-augmented test automation uses machine learning to generate, maintain, and prioritise software tests — a genuine accelerant for legacy modernisation, but not a substitute for human judgment on test intent. This piece sets out where AI-generated tests are reliable, where human review stays essential, and how to structure a testing strategy around both.
AI Vendor Risk Management: Contracts, SLAs & Liability
Embedding a third-party AI model changes your risk exposure in ways standard SaaS contracts don't cover. Here's what actually needs to be in the agreement — from SLAs to indemnity to data handling.
International Expansion Architecture for Australian SaaS
Expanding an Australian SaaS platform overseas means re-architecting infrastructure, data residency, tenancy, billing, and AI features together — not as a configuration change. This guide sets out the key architecture decisions and privacy obligations CTOs need to work through first.
Data Product Management: Treating Datasets Like Products
Most internal datasets have no owner, no documented SLA, and no versioning discipline — which is why downstream teams stop trusting them and start duplicating them. This piece lays out a lightweight framework for treating a handful of critical datasets as products, without committing to a full data mesh rebuild.
Business Continuity Planning for AI-Dependent Products
Business continuity planning for AI-dependent products means designing systems and processes so AI features keep working, in degraded form if needed, when a model provider has an outage, changes rate limits, or deprecates an API. Here's how to classify critical-path features and design failover accordingly.
Composable Commerce vs Monolith: A Decision Guide
A practical decision guide for retail and e-commerce technology teams weighing a move from monolithic platforms to composable, headless architecture — covering flexibility, cost, team capability and common migration pitfalls.
Sustainable, Cost-Efficient Cloud Architecture for Scale-Ups
Cost-efficient cloud architecture at scale starts with governance, not infrastructure choice. This piece covers the account structure, guardrails, and cost levers that keep spend under control as engineering teams grow, and how to choose between a SaaS governance layer and an embedded consultancy model.