AI fraud detection for dentists.
Dentists-grade AI fraud detection, shipped by people who've done it before.
Recalls, bookings and patient messages handled automatically, with patient information protected under the Privacy Act 2020.
What AI fraud detection actually does
Pattern-watching AI for refund abuse, chargebacks, fake reviews, employee fiddles, and odd supplier invoices. With the right workflow automated, nine hours of admin a week could drop to about one.*
*Illustrative example only, based on 9 hours a week of admin falling to about 1 hour a week. Actual results vary by workflow and business.
From one-person trades to national organisations, we find the hours :)
The questions a public sector buyer asks, answered up front: privacy impact, the NZISM, where the data lives and how an algorithm is held to account.
*Illustrative example based on nine hours a week of admin. Actual results vary by workflow and business.
The pattern across most dentists we work with.
Dental practices live and die by a full chair and a clean recall list, and both take constant admin to keep on track.
No-shows, insurance queries, and recall reminders are the recurring admin load behind every dental front desk.
An AI agency, minus the theatre.
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
How it works in New Zealand
We find the one workflow costing dentists the most hours, ship a working slice into production in weeks, prove it pays for itself, then harden it and hand it over.
Less admin.
More weekend.
No theatre.
What you get
We'd call the engagement a success when dentists are using the system without thinking about us.
- Learns your normal patterns and flags outliers
- Daily anomaly report, not a constant alert flood
- Explainable scoring so you can act with confidence
- Integrates with Xero, Shopify, and POS systems
- No annual contracts
- No shelfware or licences you do not use
Good questions, answered
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.
What's the realistic outcome for dentists?
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's the realistic timeline for AI fraud detection with a dentist?
Most dentists 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".
Can you work to the NZISM?
The NZISM is the government's information security manual and is written for vendors serving agencies as well as the agencies themselves, so government-adjacent work should be built to it.
Can it remind us about the annual return?
Yes. Every company must file an annual return with the Companies Office each year, separate from its tax return.
Talk to us about AI fraud detection
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Tell us what you're trying to do and we'll reply with how we'd build it, no obligation.
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