Canterbury · Applied AI

AI fraud detection that lives in your stack, not on a vendor's roadmap. Shipped from Canterbury.

The reason we take on work in Ashburton is that the businesses here tend to be sharper about what they want than the brief lets on. AI fraud detection for a Ashburton team almost always ends up looking different to AI fraud detection for a downtown Auckland one.

What AI fraud detection actually does

Pattern-watching AI for refund abuse, chargebacks, fake reviews, employee fiddles, and odd supplier invoices. Flags weirdness early – before it's a real problem.

  • 01 Learns your normal patterns and flags outliers
  • 02 Daily anomaly report, not a constant alert flood
  • 03 Explainable scoring so you can act with confidence
  • 04 Integrates with Xero, Shopify, and POS systems

Built on: Claude DuckDB Postgres Vercel

Canterbury

What we keep seeing in Ashburton.

  • Mid-Canterbury runs on irrigated dairy, arable, and a tight industrial sector – AI here is about turning paddock data into operational decisions.
  • Dairy, arable, and a strong engineering base supporting irrigation and processing. AI lands well when it makes long days shorter.

We work with teams across Ashburton: Ashburton CBD · Hampstead · Methven · Rakaia · Tinwald.

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How we build AI fraud detection for a Ashburton team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Ashburton business, so value lands before the build is finished. AI fraud + anomaly.

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The outcome for Ashburton teams

The shape of the result for Ashburton teams: Recovers 3-5x its cost in caught fraud within 6 months. Built on Claude, hardened with the rest of the stack as it scales.

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

How fast could we have AI fraud detection in production?

Eight to ten weeks for most Ashburton businesses. Faster if your data is in good shape and slower if we're untangling a legacy integration first. We'll give you a realistic number on the scoping call rather than the optimistic one.

What's the smallest engagement you'd take on?

A two-week paid discovery for Ashburton businesses that aren't sure whether the build is worth doing at all. You get a one-page write-up of what we'd build, what we'd skip, and what it would cost. About 30% of those discoveries end with us recommending you don't proceed.

Do you have proof this works for Ashburton businesses?

Direct case study: Recovers 3-5x its cost in caught fraud within 6 months. Happy to walk you through full numbers on a call.

What if our Ashburton doesn't have any data ready?

Most don't. Getting the data into shape – ingestion, cleaning, the lightweight contracts you need before any model is useful – is part of the engagement. For AI fraud detection specifically, we typically run that work on Claude, DuckDB, Postgres, Vercel and assume messy starting conditions from day one.

Twenty minutes, your call.

You describe what's broken. We'll tell you what we'd actually do about it.

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