AI efficiency audit that lives in your stack, not on a vendor's roadmap. Shipped from Victoria.

Our goal is to give Geelong 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 Geelong is that the businesses here tend to be sharper about what they want than the brief lets on. AI efficiency audit for a Geelong team almost always ends up looking different to AI efficiency audit for a downtown Auckland one.

What AI efficiency audit actually does

A two-week audit that maps your team's actual time spend, finds the 5 highest-leverage AI plays, and ships the first one. No vapourware, no 80-slide decks - just one working thing.

  • 01 Time-and-motion review of your top 5 workflows
  • 02 Ranked AI opportunity list - ROI, effort, risk
  • 03 One pilot shipped in week two, not a six-month roadmap
  • 04 Fixed price, fixed scope, kept honest

Built on: Claude Notion Loom Linear Vercel

What you actually get

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

How AI efficiency audit compares

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

 Hiring for itAn off-the-shelf toolKoala 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 Geelong.

  • Geelong has rebuilt itself around health, education and advanced manufacturing since the car plants closed - AI here supports a city that's already proven it can reinvent.
  • A major hospital redevelopment, Deakin University's waterfront campus, and a manufacturing base that's shifted from cars to precision and renewables work. Geelong teams want AI that fits a city used to modernising fast.

We work with teams across Geelong: Geelong CBD · Newtown · Belmont · Corio · Ocean Grove · Torquay.

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How we build AI efficiency audit for a Geelong team.

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

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How the work runs

The outcome for Geelong teams

If we build the right slice first, Geelong teams feel the difference inside the first month. Average pilot saves 8 hours/week within 30 days of launch.

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 Koala Dynamics, that drops to about 52.

Try the calculator

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

FAQ

What's the realistic timeline for AI efficiency audit with a Geelong?

Most Geelong 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 efficiency audit worth it for a smaller Geelong?

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.

Has this actually shipped for a real Geelong?

Yes. Average pilot saves 8 hours/week within 30 days of launch. We'll share comparable engagements on the call.

What happens if we want to swap a vendor out later?

AI efficiency audit 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, Notion, Loom, Linear, 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.