Insights
Product, design, AI, and engineering perspectives from our team.
Application Modernisation in Australia: The Complete 2025 Guide
A practical guide to application modernisation for Australian engineering leaders — covering patterns like strangler fig and re-architecture, architecture maturity trade-offs, and Australian-specific context including the Essential Eight and Hosting Certification Framework.
Planning an AI Engagement: What Production Delivery Requires
Before committing budget to an AI initiative, it's worth agreeing on what production-grade delivery actually means. This guide covers the standards worth setting for any AI engagement — from production track record to MLOps planning and IP ownership.
AI for Australian Manufacturing: 5 Use Cases That Work
Australian manufacturers are deploying production AI across five use cases today: predictive maintenance, computer vision quality inspection, document AI for compliance, demand forecasting, and procurement automation. This practitioner overview covers what makes each use case work in production — and where each one fails — for CTOs and engineering leaders evaluating where to start.
AI Consulting Melbourne: How to Evaluate an AI Consultancy
Evaluating an AI consultancy in Australia comes down to a few concrete questions: who actually does the work, do they have production deployments, and can they speak to Australian Privacy Principles compliance. This guide gives business leaders a practical framework for assessing fit, asking the right questions, and understanding how mid-market AI engagements are typically structured.
Fractional CTO Services in Melbourne and Australia
A fractional CTO is a senior technology executive who works with your business on a part-time retainer basis — providing strategic leadership and architecture oversight without the cost of a full-time hire. This guide covers how fractional CTO engagements work in the Australian market, what they typically cost, and how to decide whether one is right for your business.
RAG Implementation Consulting: How It Works and When to Use It
Retrieval-Augmented Generation (RAG) is an LLM architecture pattern that grounds model output in retrieved documents at inference time — making it one of the most practical approaches for enterprise knowledge retrieval. This article explains how RAG works, when it is preferable to fine-tuning, and what a production-grade implementation actually involves, including Australian data sovereignty considerations.
MLOps Consulting in Australia: From Notebook to Production
MLOps consulting helps Australian engineering teams close the gap between a model that works in a notebook and one that reliably runs in production. This guide covers the MLOps maturity model, five core capabilities, tooling options including MLflow, Kubeflow, SageMaker, and Vertex AI, and the Australian data residency and privacy obligations that affect how ML pipelines should be architected.
Custom AI Agent Development: Architecture and Use Cases
AI agents are autonomous software systems that plan, use tools, and execute multi-step tasks — a significant step beyond standard LLM calls. This guide covers the core architectural patterns, industry use cases, data prerequisites, and what a responsible commissioning process looks like for teams considering custom AI agent development.
Context Engineering for LLM Apps: Beyond Prompt Templates
Prompt templates are where LLM applications start. Context engineering is what makes them work reliably in production. This article covers the four core levers — retrieval, compression, memory, and ordering — and how to build a context pipeline that produces consistent, cost-efficient model behaviour at scale.