Cleaning companies · Applied AI

AI fraud + anomaly, built around how a cleaning company actually works.

There is a version of AI fraud detection that cleaning companies buy off a shelf and quietly stop using inside a month. Then there's the version that's wired into your real workflow, owned by a person on your team, and still in use a year later. We only build the second 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

The cleaning companies reality

What we keep hearing from NZ cleaning companies.

  • Cleaning businesses run on recurring contracts and one-off quotes, both of which need fast, consistent follow-up to win.
  • Quote turnaround and scheduling reliability are what most decide whether a cleaning company keeps a client past the first job.
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How we build AI fraud detection for cleaning companies.

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

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The outcome for cleaning companies

We'd call the engagement a success when cleaning companies 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

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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.

Questions

FAQ

What's a typical engagement length for cleaning companies?

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.

Do you do hourly billing or fixed price?

Fixed price for the pilot, every time. After that it's your call – fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.

What's the realistic outcome for cleaning companies?

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.

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.

Worth a conversation?

Even if you don't end up working with us, you'll leave the call knowing what's worth building.

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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.