Insights
Product, design, AI, and engineering perspectives from our team.
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.
Data Contracts: Stopping Pipeline Breakage Before It Starts
Silent schema drift is one of the most common and costly causes of broken data pipelines and degraded AI models — and it rarely announces itself. Data contracts are the structural mechanism that catches upstream changes before they reach production, enforcing schema, quality, and freshness expectations at the producer level. This post explains what data contracts are, how to implement them with modern tooling, and why they are foundational to reliable AI and analytics infrastructure.
AI Incident Response: What to Do When Your Model Fails in Production
When an AI model fails in production, the failure is often silent — no error code, just degrading outputs. This guide is a practical incident response playbook for ML and LLM systems: detection, severity classification, rollback, stakeholder communication, and post-incident review, built for technical leaders who need to extend their existing incident processes to cover AI-specific failure modes.
The Open Knowledge Format: AI-ready knowledge without lock-in
The Open Knowledge Format (OKF) is a vendor-neutral way to turn scattered organisational knowledge into a portable, AI-ready asset. Here's what it is, why it matters for RAG and AI readiness, and how to start.
Change Management for AI Adoption: Getting Staff to Actually Use It
AI rollouts stall not because the technology fails, but because the people side is an afterthought. This post breaks down why adoption flatlines and provides a practical eight-step change management playbook to drive genuine, sustained AI usage across your teams.
Upskilling Your Engineering Team for AI: A Practical Plan for CTOs
Most engineering teams can integrate an API — far fewer are ready to build production AI systems that are observable, compliant, and resilient. This post gives CTOs a concrete plan: how to map current capabilities, build tiered learning paths, design hands-on projects, and decide when to train versus hire.