Built for the parts of the materials scientists job that eat a Tuesday.

Every materials scientists team we've worked with has a different definition of "broken".

Life, Physical, and Social ScienceBuilt in New ZealandProduction AI, not slidewareHours back, not headcountLife, Physical, and Social ScienceBuilt in New ZealandProduction AI, not slidewareHours back, not headcountLife, Physical, and Social ScienceBuilt in New ZealandProduction AI, not slidewareHours back, not headcountLife, Physical, and Social ScienceBuilt in New ZealandProduction AI, not slidewareHours back, not headcount

If we build the right slice first, Materials Scientists feel the difference inside the first month. 0.11% of all Claude.ai conversations map to this kind of work (Anthropic Economic Index, March 2026) Kiwi Dynamics

0.11% of all Claude.ai conversations map to this kind of work (Anthropic Economic Index, March 2026).

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How the work runs

We find the one workflow costing Materials Scientists the most hours, ship a working slice into production in weeks, prove it pays for itself, then harden it and hand it over.

To start, tell us about your week

Small crew or big organisation, the busywork is the same :)

The questions a public sector buyer asks, answered up front: privacy impact, the NZISM, where the data lives and how an algorithm is held to account.

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*Illustrative example based on nine hours a week of admin. Actual results vary by workflow and business.

By the numbers

Canada's overall AI usage sits at 4.37x its expected level, with Singapore leading at 5.53x.

Adoption is already well ahead of the curve, and the edge goes to whoever turns it into a concrete workflow first.

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Not your typical AI agency.

Honest about what AI can and cannot do

Ships the one workflow that pays for itself

Hours given back, never the size of the invoice

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*Every engagement is scoped and quoted up front. Results vary by workflow and business.

What changes for materials scientists week to week

Materials Scientists sits within Science & Research, one of the sectors we build production AI for.

Summarise literature with citations
Clean and explore datasets
Draft methods and reports
Answer questions over your documents

Straight answers

Will this run on our own infrastructure?

Yes, where it makes sense. AI can sit entirely in your cloud account, with model calls routed through endpoints you control. We default to Summarise literature with citations, Clean and explore datasets, Draft methods and reports, Answer questions over your documents but the architecture supports your existing platform choices.

When does AI actually pay back?

Inside the first quarter, in our experience. We pick the first slice specifically because it's the highest-leverage workflow for a materials scientists team - so the savings start landing before the rest of the build is finished.

How do you price AI engagements?

Fixed-scope pilots first, then either project pricing or a small monthly retainer for the ongoing work. No long lock-ins, no 18-month black-box deals. Most Materials Scientists are surprised how small the first cheque is.

Can it remind us about the annual return?

Yes. Every company must file an annual return with the Companies Office each year, separate from its tax return.

Can the AI run on New Zealand-hosted cloud?

Yes, the options now exist. Microsoft opened a New Zealand cloud region in December 2024 and AWS followed in September 2025, both with local data residency.

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Tell us what you're trying to do and we'll reply with how we'd build it, no obligation.