Insights
Product, design, AI, and engineering perspectives from our team.
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.
Digital Twins for Manufacturing and Logistics in Australia
Digital twins let manufacturers and logistics operators simulate changes safely and cut downtime — but only once the right sensors and data infrastructure are in place. Here's what Australian operators need before a twin becomes viable, and where AI fits in once it is.
Data Monetisation: Turning Internal Data Into Revenue
A practical look at how growing Australian companies can turn internal data into an external revenue stream — covering commercial models, technical foundations, and the governance considerations that come first.
Real Options Thinking for Technology Investment Decisions
Fixed ROI models break down when applied to AI pilots and platform rebuilds, because they demand precision at the point of maximum uncertainty. This article sets out a real options framework that lets CTOs and CFOs approve staged technology investment without a single, unreliable upfront ROI number.