We've worked with enough operators in Logan 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.
Built and supported here - the way a Logan business would actually use it.
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
Why Logan businesses are a fit for this.
- Logan is trades, logistics and light manufacturing between Brisbane and the Gold Coast, with one of the most diverse populations in the country.
- A dense band of transport, warehousing, manufacturing and home services businesses serving two capitals at once. Multilingual customer contact and after-hours enquiries are both bigger factors here than the numbers suggest.
We work with teams across Logan: Logan Central · Springwood · Beenleigh · Browns Plains · Meadowbrook.
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How we build AI fraud detection for a Logan team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Logan business, so value lands before the build is finished. AI fraud + anomaly.
Talk to usThe outcome for Logan teams
What changes for Logan teams after this lands: the work that used to need a person stays done, the work that needs a person gets done with their attention undivided. 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
What's a typical engagement length for Logan businesses?
Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.
Are there hidden costs we should plan for?
Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.
Anyone else in this space using AI fraud detection?
Plenty. Recovers 3-5x its cost in caught fraud within 6 months. The interesting question is rarely "does it work" - it's "is your team ready to use the output." That's what we'd scope on the call.
Can you work with our existing systems?
Yes. The default AI fraud detection stack we reach for is Claude, DuckDB, Postgres, Vercel, but we'll bend it around whatever you already run - Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.
Worth a conversation?
Even if you don't end up working with us, you'll leave the call knowing what's worth building.
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.