Insights
Product, design, AI, and engineering perspectives from our team.
ISO 27001 Certification While Modernising Legacy Systems
A practical look at how Australian fintech, healthtech and insurtech companies can approach ISO 27001 certification alongside legacy infrastructure modernisation — including how it differs from SOC 2 and where the two workstreams overlap.
Observability for AI Agent Pipelines in Production
Multi-step AI agents fail in ways infrastructure metrics can't explain. This guide covers how to instrument chained model and tool calls with distributed tracing and structured logging so engineering teams can actually debug production failures and latency.
Measuring AI Feature Adoption: A Framework for SaaS Teams
Usage counts don't tell you whether an AI feature is actually working. This framework covers event design, cohort analysis, and qualitative feedback loops for measuring real adoption of AI features in SaaS products — distinct from financial ROI modelling.
AI Centre of Excellence: Three Models and How to Choose
There are three real operating models for an AI Centre of Excellence — centralised, federated, and hub-and-spoke — and the right one depends on how many AI initiatives you're running, how mature your business units are, and how much central governance you're willing to fund. This article breaks down each model's trade-offs and how to choose.
Choosing an Enterprise AI Platform: Bedrock vs Azure vs Vertex
Choosing an AI platform is a governance and architecture decision, not just a model choice. We compare AWS Bedrock, Azure AI Foundry, and Google Vertex AI on governance tooling, cost structure, and vendor lock-in — and are upfront about where public documentation runs thin.
Measuring Data Engineering Team Productivity: A Framework
Most engineering teams track DORA metrics for software delivery, but data engineering needs a different lens. This article sets out a practical framework covering throughput, pipeline reliability, and stakeholder satisfaction for growing data functions.
Edge AI for Industrial IoT: An Architecture Guide
Edge AI for industrial IoT means running inference locally on-site rather than in the cloud. This guide covers the latency, connectivity, and maintenance trade-offs that shape a sound architecture.
API Monetisation and Partner Ecosystem Architecture
API monetisation turns programmatic access to your platform into a product with its own pricing, SLAs, and go-to-market motion. This guide covers how to architect pricing, developer portals, rate limiting, and versioning for a durable partner ecosystem.
Contact Centre AI: Integrating Voice and Chat Automation
A practical look at integrating AI-driven voice and chat automation into existing telephony and CRM systems — covering integration patterns, where automation should and shouldn't replace agents, and how to measure impact honestly.