Insights
Product, design, AI, and engineering perspectives from our team.
LLM Cost Optimisation: Cutting Spend Without Cutting Quality
Cutting LLM spend doesn't have to mean cutting quality. This guide covers the practical engineering levers — model tiering, prompt caching, batching, prompt budgets, and observability — with a worked example showing how they compound.
Data Science Consulting in Melbourne: A Scoping Guide
Scoping your first data science engagement well matters more than picking the right vendor. This guide covers problem framing, data readiness checks, deliverable shapes, and pricing models for Melbourne-based mid-market companies.
Enterprise AI Chatbots in 2026: Build vs Buy
Enterprise chatbots in 2026 aren't a simple build-vs-buy call — managed platforms like Google's Gemini Enterprise Agent Platform now handle session state, RAG and governance out of the box. We break down when buying wins, when custom builds win, and the mid-market sweet spot in between.
DORA Metrics for Mid-Market Engineering Teams
DORA metrics are the clearest read on engineering delivery performance, but most guidance is written for large platform teams. Here's how a 10-50 person engineering team can instrument DORA cheaply, benchmark realistically, and account for AI-assisted development shifting the baseline.
AI Governance Framework for Australian Mid-Market Companies
Most AI governance frameworks assume an enterprise compliance team the mid-market doesn't have. Here's a right-sized approach — a lean policy set, model inventory, human-in-the-loop rules and vendor due diligence — mapped to Privacy Act reform and the Voluntary AI Safety Standard.
ML Proof of Concept to Production: Why Most Stall
Most ML proof of concepts never reach production — not because the model is wrong, but because reproducibility, deployment, monitoring, evaluation, versioning, and lineage were never built. Here's why POCs stall and what a realistic path to production looks like.
Legacy System Modernisation: A Phased Roadmap
Legacy system modernisation doesn't have to mean choosing between a risky big-bang rewrite and years of technical debt. This guide sets out a phased, strangler-fig roadmap with funding gates, risk controls, and how AI-assisted code understanding changes the economics of the assessment phase.
MLOps Consulting: What Australian Mid-Market Teams Actually Need
Enterprise MLOps advice rarely fits a mid-market team running a handful of models with a small data team. Here's what a right-sized MLOps consulting engagement covers — minimum viable stack, build-vs-buy tooling, team shape, and a maturity model.
AI Security Review: Threat Modelling LLM Apps Before Launch
A practical, pre-launch framework for threat modelling LLM applications — prompt injection, tool-use data exfiltration, RAG poisoning, and tenant isolation — mapped to OWASP's LLM Top 10 and Australian privacy obligations.