Canterbury · Applied AI

AI fraud detection designed around the way a Timaru team actually runs.

Timaru sits in a regional context that genuinely changes the build. Connectivity assumptions, the rhythm of the working week, the proximity of your team to your customers – none of those are details our default AI fraud detection template would catch.

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

The pattern across Timaru engagements we've shipped.

  • South Canterbury runs on port logistics, food processing, and dairy – AI here is about coordinating shifts and shipments with fewer phone calls.
  • Port of Timaru, food processors, dairy, and a wide trades sector. AI tools that integrate with existing ERP and POS land best.

We work with teams across Timaru: Timaru CBD · Marchwiel · Highfield · Geraldine · Temuka · Pleasant Point.

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How we build AI fraud detection for a Timaru team.

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

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The outcome for Timaru teams

We'd call the engagement a success when Timaru teams are using the system without thinking about us. Recovers 3-5x its cost in caught fraud within 6 months.

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 the realistic timeline for AI fraud detection with a Timaru?

Most Timaru businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational – your team gets to use the thing well before the engagement is "done".

What does AI fraud detection cost for a Timaru?

Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.

Can you walk us through a comparable build?

Yes – on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Recovers 3-5x its cost in caught fraud within 6 months.

What happens if we want to swap a vendor out later?

AI fraud detection is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Claude, DuckDB, Postgres, Vercel are our defaults, but the build is intentionally portable.

One reply, one direction.

We don't run sequences or follow-up automation. One useful answer, one decision on your side.

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