Enterprise AI knowledge. From a team who ships it.

In 2014, Samoa's health system had no centralised monitoring framework. We built one. Here's what it took and what it taught me about data infrastructure.

Where genetics, AI, and nutrition intersect. The science is real - and the practical applications are here today.

When health data crosses borders or bypasses community governance, the consequences aren't technical. They're cultural and clinical.

The gap between demo and production AI performance is infrastructure. Here's how to benchmark the layer that determines speed, reliability, and cost.

80,000+ knowledge assets in a governed RAG architecture. The Hakamana AI thesis: Indigenous-led, enterprise-grade, values-driven.

Beyond benchmarks. How to measure AI impact in enterprise context, where academic metrics don't capture what matters.

Iwi health providers have specific needs that mainstream health IT consistently ignores. It's time the technology caught up.

Enterprise AI impact measurement that goes beyond vanity metrics. How to build measurement frameworks with proper statistical foundations.

Evaluation is not a phase at the end. It is baked into the design from day one. An evidence-based approach to AI systems that know how well they are working.

Enterprise AI vendors claim extraordinary results. The evidence rarely supports them. Here's how to evaluate AI claims with statistical confidence.

Health AI research requires specific methodological rigour. How to design evaluation studies that produce trustworthy evidence for health AI systems.

AI models degrade in production. Statistical rigour meets operational pragmatism in this guide to detecting, measuring, and fixing model drift.