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Latest News

Winners of the World Bank Climate Risk Challenge 

A big congratulations to Léa Cassé and the DelphiQ team who were recently announced as the winners of the Global Industry Challenge – World Bank track at the Quantum World Congress 2025 for their innovative solution to improving insurance for communities exposed to flooding.

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October e-newsletter

October 9, 2025
As an initiative supported by the Ministry of Business, Innovation and Employment (MBIE), TAIAO is committed to promoting and facilitating the adoption of artificial intelligence (AI) and data science in New Zealand's environmental sector.In this newsletter, you’ll find:Delphitech wins global awar...
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Hackathon 2025

The 2025 AI Hackathon was a big success!

This year's Waikato Hackathon event saw 17 participants across four teams developing a range of innovative AI solutions to real world environmental problems.

Team Gallagher were the winning team with their project Recraft AI: Turning Commercial Waste into Community Treasure which proposed a system to reduce and upcycle construction waste through community-led initiatives.

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TAIAO Talks

Get the inside scoop on the projects shaping our work. In this new series, our team members share deeper insights into the ideas, challenges, and impact behind the scenes.

This week, dive into CAPYmoa with Heitor Murilo Gomes and discover how it's making waves.

An initiative supported by the Ministry of Business, Innovation and Employment (MBIE), TAIAO is committed to promoting and facilitating the adoption of artificial intelligence (AI) and data science in New Zealand's environmental sector. 

Recognising that good data is essential to research, understand, and set policy for effective management of our natural environment, the team at TAIAO are working to develop new machine learning methods that are tailored to process environmental data gathered from our New Zealand context.

TAIAO uses AI models to analyse vast amounts of historical and real-time data, and identify patterns to predict future trends, such as extreme weather events. Through this mahi, we aim to break barriers by enhancing industry capability, and pioneer new machine learning methods to process vast amounts of data in real-time to meet today's pressing demands.

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