Insights
Product, design, AI, and engineering perspectives from our team.
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.
LLM Evaluation in Production: A Three-Layer Approach
Shipping an LLM feature is the easy part. Knowing whether it still works correctly six weeks later — after a prompt change, a model version bump, or a shift in user behaviour — is where most teams struggle. This post covers a three-layer evaluation approach that gives engineering teams real confidence in production LLM systems.
AWS SageMaker vs Self-Hosted GPU Serving: Cost and Control
AWS SageMaker and self-hosted GPU serving on A100 or H100 hardware each make sense under different conditions — and the wrong choice becomes expensive quickly. This article breaks down the cost structure, operational trade-offs, and decision framework for Australian engineering teams moving ML models to production.
AI Readiness Assessment: A Practical Guide for Australian Businesses
An AI readiness assessment is the structured process of evaluating whether your organisation has the data, infrastructure, talent, and cultural foundations needed to adopt AI successfully. This guide covers the five readiness dimensions, maturity models, and a practical assessment framework for Australian businesses considering AI adoption.
AI Readiness Assessment: A Complete Guide for Australian Businesses
An AI readiness assessment is the structured diagnostic that tells Australian businesses what they actually need before investing in AI development. This guide covers the six readiness dimensions, Australian regulatory obligations, cost models, and how to build a credible implementation roadmap from the assessment results.
Digital Engineering Consultancies in Australia: A Market Guide
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