New Territories · Applied AI
AI fraud detection in Sha Tin
AI fraud detection that lives in your stack, not on a vendor's roadmap. Shipped from New Territories.
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.
The reason we take on work in Sha Tin is that the businesses here tend to be sharper about what they want than the brief lets on. AI fraud detection for a Sha Tin team almost always ends up looking different to AI fraud detection for a downtown Auckland one.
- 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
Why Sha Tin businesses choose this
Sha Tin anchors the New Territories' manufacturing, logistics and science park economy – AI here means industrial-scale operations work, not office admin.
Our field notes from Sha Tin builds.
Hong Kong Science Park and a broad manufacturing and logistics base serving cross-border trade with mainland China sit here. Businesses want AI that handles scheduling, compliance and cross-border logistics without becoming the bottleneck.
We work with teams across Sha Tin: Science Park · Tai Wai · Fo Tan · Ma On Shan.
How we build AI fraud detection for a Sha Tin team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Sha Tin business, so value lands before the build is finished. Every engagement starts with a short call and a paid discovery if the brief needs one.
AI fraud + anomaly.
The outcome for Sha Tin teams
If we build the right slice first, Sha Tin teams feel the difference inside the first month. Recovers 3-5x its cost in caught fraud within 6 months.
Recovers 3-5x its cost in caught fraud within 6 months.
AI fraud detection in Sha Tin – common questions
How fast could we have AI fraud detection in production?
Eight to ten weeks for most Sha Tin businesses. Faster if your data is in good shape and slower if we're untangling a legacy integration first. We'll give you a realistic number on the scoping call rather than the optimistic one.
What's the smallest engagement you'd take on?
A two-week paid discovery for Sha Tin 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.
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.
What if our Sha Tin doesn't have any data ready?
Most don't. Getting the data into shape – ingestion, cleaning, the lightweight contracts you need before any model is useful – is part of the engagement. For AI fraud detection specifically, we typically run that work on Claude, DuckDB, Postgres, Vercel and assume messy starting conditions from day one.
One short call.
Tell us what you're trying to fix. We'll come back inside a working day.
Get in touch
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.