AI fraud detection designed around the way a Los Angeles team actually runs.

Our goal is to give Los Angeles businesses a three-day weekend, so people can spend more time with their families and the people they love :)

The reason we take on work in Los Angeles is that the businesses here tend to be sharper about what they want than the brief lets on. AI fraud detection for a Los Angeles team almost always ends up looking different to AI fraud detection for a downtown Auckland one.

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

What you actually get

Every engagement is scoped and quoted up front. This is what is in the box.

How AI fraud detection compares

The two things most businesses do instead, and where each one runs out.

 Hiring for itAn off-the-shelf toolKiwi Dynamics
Fit to how you workFits perfectly, costs a salaryYou bend your process to suit the toolBuilt around the workflow you already run
Time to something usefulImmediate, and permanentQuick to switch on, slow to make fitA working slice in weeks, then hardened
Who owns the dataYou doThe vendor, on the vendor's termsYou do, in your own accounts
When it breaksThat person sorts it, if they are inA support queue and a ticket numberThe people who built it
What it costsA salary, every year, foreverPer seat, forever, used or notScoped and quoted up front

What we keep seeing in Los Angeles.

  • LA runs on entertainment, production and a sprawling small business economy across a huge metro area - AI here has to work for a one-location shop and a studio with the same reliability.
  • Film and TV production, a massive creative freelance economy, and small businesses spread across a metro area bigger than most countries. LA teams want AI that handles the admin so they can stay on the thing that actually makes money.

We work with teams across Los Angeles: Downtown LA · Hollywood · Santa Monica · Culver City · Burbank · Venice.

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How we build AI fraud detection for a Los Angeles team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Los Angeles business, so value lands before the build is finished. AI fraud + anomaly.

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How the work runs

The outcome for Los Angeles teams

The shape of the result for Los Angeles 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

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*Every engagement is scoped and quoted up front. Results vary by workflow and business.

How much is not automating costing you?

Nine hours a week of admin is 468 hours a year. With Kiwi Dynamics, that drops to about 52.

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*Based on 9 hours a week of admin at Kiwi Dynamics' typical 80% automation rate. Your number may vary, the calculator uses your own.

FAQ

What's the realistic timeline for AI fraud detection with a Los Angeles?

Most Los Angeles businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational - your team gets to use the thing well before the engagement is "done".

Is AI fraud detection worth it for a smaller Los Angeles?

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 Los Angeles 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 happens if we want to swap a vendor out later?

AI fraud detection is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Claude, DuckDB, Postgres, Vercel are our defaults, but the build is intentionally portable.

Sketch this with us.

We'll map your real workflow before quoting anything.

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.