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

Our goal is to give Hobart businesses a three-day weekend, so people can spend more time with their families and the people they love :)

The reason we take on work in Hobart is that the businesses here tend to be sharper about what they want than the brief lets on. AI fraud detection for a Hobart 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

What you actually get

Every engagement is scoped and quoted up front. This is what is in the box.

How AI fraud detection compares

The two things most businesses do instead, and where each one runs out.

 Hiring for itAn off-the-shelf toolKoala Dynamics
Fit to how you workFits perfectly, costs a salaryYou bend your process to suit the toolBuilt around the workflow you already run
Time to something usefulImmediate, and permanentQuick to switch on, slow to make fitA working slice in weeks, then hardened
Who owns the dataYou doThe vendor, on the vendor's termsYou do, in your own accounts
When it breaksThat person sorts it, if they are inA support queue and a ticket numberThe people who built it
What it costsA salary, every year, foreverPer seat, forever, used or notScoped and quoted up front

What we keep seeing in Hobart.

  • Hobart runs on tourism, aquaculture and a small but sharp professional services scene - AI here has to work for teams that don't have a big back office to absorb a bad tool.
  • Salmon and seafood exports, a tourism season built around MONA and the wider arts scene, and a compact CBD professional layer. Hobart businesses need AI that's genuinely easy to run without dedicated IT staff.

We work with teams across Hobart: Hobart CBD · Battery Point · Glenorchy · Kingston · Sandy Bay · Moonah.

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How we build AI fraud detection for a Hobart team.

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

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How the work runs

The outcome for Hobart teams

If we build the right slice first, Hobart teams feel the difference inside the first month. 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 Koala Dynamics, that drops to about 52.

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

FAQ

What's the realistic timeline for AI fraud detection with a Hobart?

Most Hobart 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 Hobart?

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.

Has this actually shipped for a real Hobart?

Yes. Recovers 3-5x its cost in caught fraud within 6 months. We'll share comparable engagements on the call.

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.

Sketch this with us.

We'll map your real workflow before quoting anything.

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.