Insights
Product, design, AI, and engineering perspectives from our team.
AI in Australian Financial Services: Risk and Compliance
AI offers Australian financial services firms real capability in fraud detection, compliance automation, and customer operations — but only when deployed with a clear view of APRA, ASIC, and Privacy Act obligations. This article covers the primary use cases, the regulatory landscape, and the technical foundations required to get AI into production in a regulated environment.
AI in Australian Professional Services: Legal and Advisory
Mid-market Australian law firms, accounting practices, and advisory businesses are applying AI to document review, research, and client delivery — but the gap between a demo and a production system that handles client data responsibly is significant. This article covers practical use cases, Australian Privacy Principles obligations, and how to think honestly about ROI without overclaiming.
Data Infrastructure Consulting: Building the AI Foundation
Most growing Australian organisations don't have a data problem — they have a data infrastructure problem. This post covers the modern data stack (Snowflake, BigQuery, Databricks), ELT vs ETL trade-offs, data contract patterns, and Australian data sovereignty considerations, with a practical look at consolidating five warehouses into one.
Technical Debt and AI: Why You Can't Build on a Broken Foundation
Layering AI onto systems carrying significant technical debt doesn't hide the debt — it amplifies it. This post covers how to audit the specific foundations your AI use case depends on, prioritise remediation without a full rewrite, and sequence modernisation and AI adoption so your investment actually reaches production.
AI Product Strategy: A Practical Guide for Australian Companies
A practical guide to AI product strategy for Australian mid-market companies — covering frameworks for identifying AI value, structuring a strategy engagement, common failure modes, and what to look for when selecting an external partner.
Building AI Products on Supabase, PostgreSQL and pgvector
For lean engineering teams building AI features, Supabase and PostgreSQL have become a serious default stack. This article covers why the combination works, where pgvector fits into RAG architectures, how row-level security keeps AI features production-safe, and what the trade-offs look like as you scale.
Snowflake vs BigQuery for Australian Mid-Market Data Platforms
Snowflake and BigQuery are the two dominant cloud data warehouses for mid-market teams in Australia, but the right choice depends on your cloud estate, data-residency obligations, cost profile, and AI roadmap. This guide cuts through the noise to help Australian data and engineering leaders make a defensible decision.
MLflow vs Weights & Biases: Experiment Tracking and Model Registry
MLflow and Weights & Biases are the two platforms most growing ML teams evaluate for experiment tracking and model registry. This guide compares them honestly across deployment model, data residency, collaboration, and production reproducibility — so you can make the right call for your team and regulatory context.
PyTorch vs TensorFlow in 2025: Choosing a Production ML Framework
PyTorch and TensorFlow are both production-capable ML frameworks in 2025, but they suit different teams, workloads, and deployment environments. This guide helps technical leaders make a defensible framework choice based on ecosystem fit, serving requirements, and team context — not benchmarks or hype.