We've worked with enough operators in Wānaka to know that the brief that arrives in our inbox is rarely the brief that ends up shipped. The first thing we do on any AI fraud detection project is sit with your team for a day before we propose anything.
AI fraud + anomaly, built for businesses operating in Wānaka.
Our goal is to give Wānaka businesses a three-day weekend, so people can spend more time with their families and the people they love :)
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
Without Kiwi Dynamics
With Kiwi Dynamics
What you actually get
Every engagement is scoped and quoted up front. This is what is in the box.
- A working pilot, in productionNot a prototype on someone's laptop. The first slice of AI fraud detection runs against real work within weeks.
- Your data stays yoursIt runs on your accounts and your tools. If we part ways you keep the system and everything in it.
- The workflow mapped before codeWe write down what good looks like for Wānaka businesses first, so nobody is guessing at handover.
- Support after it landsThe people who built it stay reachable when the business changes shape around it.
How AI fraud detection compares
The two things most businesses do instead, and where each one runs out.
| Hiring for it | An off-the-shelf tool | Kiwi Dynamics | |
|---|---|---|---|
| Fit to how you work | Fits perfectly, costs a salary | You bend your process to suit the tool | Built around the workflow you already run |
| Time to something useful | Immediate, and permanent | Quick to switch on, slow to make fit | A working slice in weeks, then hardened |
| Who owns the data | You do | The vendor, on the vendor's terms | You do, in your own accounts |
| When it breaks | That person sorts it, if they are in | A support queue and a ticket number | The people who built it |
| What it costs | A salary, every year, forever | Per seat, forever, used or not | Scoped and quoted up front |
Where Wānaka operators actually lose hours.
- Wānaka has the boom-and-shoulder rhythm of a lifestyle town - AI here helps small teams handle big seasonal swings without burning out.
- Tourism, hospitality, lifestyle property, and trades feeding constant builds. AI shines when it lets a small operator look like a big one.
We work with teams across Wānaka: Wānaka township · Albert Town · Hāwea · Cardrona · Lake Hāwea.
Talk to us about this →
How we build AI fraud detection for a Wānaka team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Wānaka business, so value lands before the build is finished. AI fraud + anomaly.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Wānaka businesses the most hours or the most leads, and deliberately ignore the rest for now.
- Ship a working sliceA narrow version goes into production in weeks, against real work, so the value shows up before the build is finished.
- Prove it, then widenWe measure it against what the work cost before. If it does not pay for itself, we say so rather than scaling it.
- Harden and hand overLogging, fallbacks and a real handover, so it keeps running when we are not in the room.
The outcome for Wānaka teams
We'd call the engagement a success when Wānaka 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
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
FAQ
When does AI fraud detection 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 Wānaka - so the savings start landing before the rest of the build is finished.
Do you do hourly billing or fixed price?
Fixed price for the pilot, every time. After that it's your call - fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.
Do you have proof this works for Wānaka 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.
Will this run on our own infrastructure?
Yes, where it makes sense. AI fraud detection can sit entirely in your cloud account, with model calls routed through endpoints you control. We default to Claude, DuckDB, Postgres, Vercel but the architecture supports your existing platform choices.
Skip the pitch.
Tell us the workflow and we'll come back with what we'd build first.
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.