AI fraud + anomaly - wired into an Austin workflow, not bolted on the side.

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

Austin 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.

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 Austin teams tell us when they get on a call.

  • Austin is the tech and startup capital of Texas, and every business here has already seen an AI pitch - AI that ships is the only kind that gets taken seriously.
  • A dense startup and venture scene, major tech company offices, and a live-music and hospitality economy layered on top. Austin teams have high AI literacy already, so the bar is a working product, not a pitch deck.

We work with teams across Austin: Downtown Austin · South Congress · East Austin · The Domain · Round Rock · Cedar Park.

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How we build AI fraud detection for an Austin team.

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

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How the work runs

The outcome for Austin teams

Recovers 3-5x its cost in caught fraud within 6 months. For Austin 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

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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.

Try the calculator

*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 an Austin?

Most Austin 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 Austin?

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.

What's the realistic outcome for Austin businesses?

Recovers 3-5x its cost in caught fraud within 6 months. We don't promise tenfold lifts because we don't see them outside of marketing decks.

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.

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.