Sydney 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.
AI fraud detection that lives in your stack, not on a vendor's roadmap. Shipped from New South Wales.
Our goal is to give Sydney 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 Koala Dynamics
With Koala 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 Sydney 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 | Koala 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 |
The pattern across Sydney engagements we've shipped.
- Sydney runs on finance, professional services and a tech scene chasing Melbourne for the national crown - AI here has to survive a genuinely competitive market, not just look good in a pitch.
- ASX-listed HQs, the big four banks, and a startup corridor from Surry Hills to Chippendale sit alongside a huge professional services layer of legal, accounting and consulting firms. Sydney businesses have already tried AI once, so we build for teams who want the second attempt to actually stick.
We work with teams across Sydney: CBD · North Sydney · Parramatta · Surry Hills · Chatswood · Sydney Olympic Park.
Talk to us about this →
How we build AI fraud detection for a Sydney team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Sydney 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 Sydney 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 Sydney teams
Recovers 3-5x its cost in caught fraud within 6 months. For Sydney teams, that almost always shows up as fewer interruptions and a calmer week, not a dashboard chart.
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
How quickly can we see something running?
Week three for a clickable internal demo against real data. Week six for a slice your team can actually use. We hold ourselves to those numbers because they're what stops a project drifting into "endless discovery".
What's the smallest engagement you'd take on?
A two-week paid discovery for Sydney 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.
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 tools do you build AI fraud detection on?
For AI fraud detection we usually reach for Claude, DuckDB, Postgres, Vercel. We're tool-agnostic at heart - we pick what your Sydney team can actually run after we hand the build over, not what looks good on a vendor sticker.
One reply, one direction.
We don't run sequences or follow-up automation. One useful answer, one decision on your side.
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.