AI data analytics designed around the way a Darwin team actually runs.

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

Darwin businesses don't need another generic AI pitch. AI data analytics only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Northern Territory.

What AI data analytics actually does

Stop digging through dashboards. Ask plain-English questions of your sales, jobs, and customer data - get charts, summaries, and the why behind the numbers in seconds.

  • 01 Natural-language queries over your Xero, Shopify, CRM data
  • 02 Weekly auto-summaries delivered to inbox or Slack
  • 03 Anomaly detection - flags weird weeks before you notice
  • 04 Forecasts that explain themselves, not black boxes

Built on: DuckDB Claude Metabase BigQuery Vercel AI SDK

What you actually get

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

How AI data analytics 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

Our field notes from Darwin builds.

  • Darwin runs on defence, resources and a tourism season split hard by the wet - AI here means handling seasonal swings without carrying seasonal headcount.
  • A major defence presence, gas and resources projects, and a tourism trade that lives and dies by the dry season. Darwin businesses want AI that scales staffing-heavy work up and down without the overhead of actually hiring for it.

We work with teams across Darwin: Darwin CBD · Palmerston · Casuarina · Nightcliff · Parap · Stuart Park.

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How we build AI data analytics for a Darwin team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Darwin business, so value lands before the build is finished. Ask your data in English.

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How the work runs

The outcome for Darwin teams

We'd call the engagement a success when Darwin teams are using the system without thinking about us. Owners check the business in 2 minutes instead of 2 hours.

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 data analytics with a Darwin?

Most Darwin 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 data analytics worth it for a smaller Darwin?

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.

Can you walk us through a comparable build?

Yes - on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Owners check the business in 2 minutes instead of 2 hours.

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

AI data analytics 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. DuckDB, Claude, Metabase, BigQuery, Vercel AI SDK 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.