The reason we take on work in Cyberport is that the businesses here tend to be sharper about what they want than the brief lets on. AI fraud detection for a Cyberport team almost always ends up looking different to AI fraud detection for a downtown Auckland one.
AI fraud detection that lives in your stack, not on a vendor's roadmap. Shipped from Hong Kong Island.
Our goal is to give Cyberport 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 Cyberport 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 |
Our field notes from Cyberport builds.
- Cyberport is Hong Kong's dedicated tech and fintech precinct, purpose-built to house the city's startup and digital economy - AI here is judged by founders and engineers, not procurement committees.
- A government-backed tech park concentrating fintech, AI and digital media startups alongside venture capital and accelerator programs. Businesses here expect AI built with real technical rigor, since many of them build software themselves.
We work with teams across Cyberport: Pok Fu Lam · Telegraph Bay · Wah Fu · Aberdeen.
Talk to us about this →
How we build AI fraud detection for a Cyberport team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Cyberport 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 Cyberport 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 Cyberport teams
The shape of the result for Cyberport teams: Recovers 3-5x its cost in caught fraud within 6 months. Built on Claude, hardened with the rest of the stack as it scales.
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".
Is AI fraud detection worth it for a smaller Cyberport?
Often, yes - and counterintuitively the ROI is sometimes faster than for the big end of town because there's less integration overhead. We'll tell you honestly on the scoping call if it isn't.
Do you have proof this works for Cyberport 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.
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 Cyberport team can actually run after we hand the build over, not what looks good on a vendor sticker.
Twenty minutes, your call.
You describe what's broken. We'll tell you what we'd actually do about it.
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.