AI by role · New Zealand & Australia

Built for the parts of the climate change analysts job that eat a Tuesday.

Every climate change analysts team we've worked with has a different definition of "broken". AI only earns its keep when it solves the specific definition you'd give it on a bad day – which is why our first call is mostly listening.

By the numbers

0.02% of all Claude.ai conversations map to this kind of work (Anthropic Economic Index, March 2026). For context, Australia's overall AI usage sits at 4.11× its expected level and Canada at 4.37× (with Singapore leading at 5.53×) — adoption is already well ahead of the curve, so the edge goes to whoever turns it into a concrete workflow first.

Climate Change Analysts sits within Science & Research, one of the sectors we build production AI for.

Where AI helps

What changes for climate change analysts week to week.

  • Summarise literature with citations.
  • Clean and explore datasets.
  • Draft methods and reports.
  • Answer questions over your documents.
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How we build AI for climate change analysts.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your team, so value lands before the build is finished. The durable wins augment a person rather than replace them — a human approves anything that matters.

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The outcome for Climate Change Analysts

If we build the right slice first, Climate Change Analysts feel the difference inside the first month. 0.02% of all Claude.ai conversations map to this kind of work (Anthropic Economic Index, March 2026)

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.

How much is not automating costing you?

Nine hours a week of admin is 468 hours a year. With Kiwi Dynamics, that drops to about 52.

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*Based on 9 hours a week of admin at Kiwi Dynamics' typical 80% automation rate. Your number may vary, the calculator uses your own.

Questions

FAQ

What's a typical engagement length for Climate Change Analysts?

Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.

Are there hidden costs we should plan for?

Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.

Has this actually shipped for a real climate change analysts team?

Yes. 0.02% of all Claude.ai conversations map to this kind of work (Anthropic Economic Index, March 2026) We'll share comparable engagements on the call.

Can you work with our existing systems?

Yes. The default AI stack we reach for is Summarise literature with citations, Clean and explore datasets, Draft methods and reports, Answer questions over your documents, but we'll bend it around whatever you already run – Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.

The honest version of a sales call.

No deck. No discovery doc. Just whether this is worth building and what it would cost.

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Talk to us about this

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