Insights
Product, design, AI, and engineering perspectives from our team.
AI Consulting vs In-House: When to Outsource vs Build Your Team
Choosing between AI consulting and building an in-house team depends on your timeline, budget, and strategic priorities. Most successful AI adoptions use a hybrid approach: consultants for initial development and knowledge transfer, followed by internal teams for ongoing evolution.
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 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.
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.
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.
AI Agents That Work: Architecture Patterns for Multi-Agent Systems
Multi-agent AI systems are becoming production reality for Australian enterprises, but most implementations fail due to poor architecture choices. Learn the orchestration patterns, communication protocols, and error handling strategies that separate proof-of-concept demos from production-ready systems.
On-Device AI for Mobile Apps: When Edge Beats Cloud
On-device AI processes machine learning directly on mobile devices, delivering sub-100ms response times and offline functionality. This guide covers Core ML, TensorFlow Lite, model optimisation, and real-world applications for Australian mobile development.
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.
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.