New York 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 designed around the way a New York team actually runs.
Our goal is to give New York 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 New York 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 |
What New York teams tell us when they get on a call.
- New York runs on finance, law, media and a professional services machine that bills by the hour - AI here earns its place by giving that time back, not by being impressive in a demo.
- Wall Street firms, Big Law, and a media and advertising industry that never fully stops, all sitting on top of the highest labour costs in the country. New York teams want AI that survives contact with a real client deadline.
We work with teams across New York: Manhattan · Brooklyn · Queens · Midtown · Financial District · Long Island City.
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How we build AI fraud detection for a New York team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your New York 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 New York 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 New York teams
Recovers 3-5x its cost in caught fraud within 6 months. For New York 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 fast could we have AI fraud detection in production?
Eight to ten weeks for most New York 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 does AI fraud detection cost for a New York?
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.
Do you have proof this works for New York 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 if our New York 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.
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.