The reason we take on work in Wānaka is that the businesses here tend to be sharper about what they want than the brief lets on. AI demand forecasting for a Wānaka team almost always ends up looking different to AI demand forecasting for a downtown Auckland one.
AI sales + stock forecasting - wired into a Wānaka workflow, not bolted on the side.
Our goal is to give Wānaka businesses a three-day weekend, so people can spend more time with their families and the people they love :)
What AI demand forecasting actually does
Forecasts that account for school holidays, NZ weather, tourist seasons, and your own promo calendar. Order the right stock, roster the right hours, plan the next quarter with actual numbers.
- 01 Combines your sales history with weather, calendar, and event data
- 02 Per-SKU and per-store forecasts, not whole-business averages
- 03 Re-forecasts weekly as new data comes in
- 04 Explains the why behind every number
Built on: Prophet DuckDB Claude BigQuery 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 demand forecasting 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 Wānaka 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 demand forecasting 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 we keep seeing in Wānaka.
- Wānaka has the boom-and-shoulder rhythm of a lifestyle town - AI here helps small teams handle big seasonal swings without burning out.
- Tourism, hospitality, lifestyle property, and trades feeding constant builds. AI shines when it lets a small operator look like a big one.
We work with teams across Wānaka: Wānaka township · Albert Town · Hāwea · Cardrona · Lake Hāwea.
Talk to us about this →
How we build AI demand forecasting for a Wānaka team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Wānaka business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Wānaka 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 Wānaka teams
The shape of the result for Wānaka teams: Stockouts down 35%, overstock down 22% in the first season. Built on Prophet, hardened with the rest of the stack as it scales.
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
How quickly can we see something running?
Week three for a clickable internal demo against real data. Week six for a slice your team can actually use. We hold ourselves to those numbers because they're what stops a project drifting into "endless discovery".
Is AI demand forecasting worth it for a smaller Wānaka?
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.
What's the realistic outcome for Wānaka businesses?
Stockouts down 35%, overstock down 22% in the first season. We don't promise tenfold lifts because we don't see them outside of marketing decks.
What tools do you build AI demand forecasting on?
For AI demand forecasting we usually reach for Prophet, DuckDB, Claude, BigQuery, Vercel. We're tool-agnostic at heart - we pick what your Wānaka team can actually run after we hand the build over, not what looks good on a vendor sticker.
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.