We've worked with enough operators in San Francisco to know that the brief that arrives in our inbox is rarely the brief that ends up shipped. The first thing we do on any AI fraud detection project is sit with your team for a day before we propose anything.
AI fraud + anomaly, built for businesses operating in San Francisco.
Our goal is to give San Francisco 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 San Francisco 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's different about doing this work in San Francisco.
- San Francisco is where the AI industry itself is headquartered - any AI pitched here is judged against the frontier labs a few blocks away, not against a competitor's landing page.
- The highest concentration of AI research labs and startups anywhere in the world, sitting alongside legacy finance and professional services firms adopting AI for the first time. San Francisco is the least forgiving market for a weak AI product, and the best one for a genuinely strong one.
We work with teams across San Francisco: Financial District · SoMa · Mission District · Marina · Nob Hill · Hayes Valley.
Talk to us about this →
How we build AI fraud detection for a San Francisco team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your San Francisco 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 San Francisco 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 San Francisco teams
We'd call the engagement a success when San Francisco teams are using the system without thinking about us. Recovers 3-5x its cost in caught fraud within 6 months.
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
What's a typical engagement length for San Francisco businesses?
Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.
Are there hidden costs we should plan for?
Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.
Has this actually shipped for a real San Francisco?
Yes. Recovers 3-5x its cost in caught fraud within 6 months. We'll share comparable engagements on the call.
Can you work with our existing systems?
Yes. The default AI fraud detection stack we reach for is Claude, DuckDB, Postgres, Vercel, but we'll bend it around whatever you already run - Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.
The honest version of a sales call.
No deck. No discovery doc. Just whether this is worth building and what it would cost.
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.