Insights
Product, design, AI, and engineering perspectives from our team.
The Strangler Fig Pattern: Modernise Legacy Apps Without Rewrites
The Strangler Fig pattern lets you modernise legacy applications gradually by routing traffic to new services while keeping old systems running. This approach reduces risk compared to complete rewrites while delivering value incrementally throughout the migration process.
The Mid-Market AI Readiness Guide: 5 Things to Fix Before You Build
Most mid-market companies rush into AI implementation without addressing fundamental readiness issues. This practical guide covers the five critical gaps that distinguish successful AI implementations from expensive failures — from messy data reality to missing success metrics.
MLOps Explained: How Production AI Stays Reliable After Launch
MLOps ensures AI models remain accurate and reliable in production through continuous monitoring, automated retraining, and governance frameworks. Learn how to detect model drift, implement monitoring pipelines, and build automated retraining systems that keep production AI performing at peak effectiveness.
What Is an AI Agent? A Plain-English Guide for Business Leaders
AI agents are software that perceive their environment, make decisions, and take action independently — going beyond chatbots and automation to handle complex business processes. This guide explains how they work and where they create real business value.
How to Implement AI in Your Business: A Guide for Australian Companies
A comprehensive implementation guide for Australian businesses looking to adopt AI. Covers the complete journey from discovery through operations, including team structure, budget planning, and regulatory considerations.
Building Production RAG Systems: Beyond the Demo to Reliable Scale
Most RAG demos work beautifully with perfect documents and cherry-picked queries. Production RAG systems face messy reality — document diversity, edge cases, and 99%+ accuracy expectations that require systematic engineering across chunking, embedding, retrieval, and monitoring.
The Real Cost of AI Implementation in Australia: Budget Guide
AI implementation costs in Australia range from $25,000 for simple chatbots to $500,000+ for complex systems. Here's transparent pricing across discovery, build, and operations phases, with real budget ranges by project type.
RAG vs Fine-Tuning: When to Use Each (And When You Don't Need Either)
RAG and fine-tuning serve different purposes in LLM deployment, with distinct cost, performance, and maintenance profiles. Most organisations jump to complex solutions when simple prompt engineering would suffice.
How to Choose an AI Consultancy in Australia: 8 Key Questions
Choosing an AI consultancy is fundamentally different from hiring traditional software developers. Here are eight critical questions to evaluate AI consultancies before you sign, covering IP ownership, production metrics, data infrastructure, and Australian compliance requirements.