The reason we take on work in Griffith is that the businesses here tend to be sharper about what they want than the brief lets on. AI demand forecasting for a Griffith team almost always ends up looking different to AI demand forecasting for a downtown Auckland one.
AI demand forecasting that lives in your stack, not on a vendor's roadmap. Shipped from New South Wales.
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 Koala Dynamics
With Koala Dynamics
The pattern across Griffith engagements we've shipped.
- Griffith is irrigated horticulture and wine in the Riverina, where water, season and freight decide the year.
- Citrus, wine, rice and food processing built on the Murrumbidgee Irrigation Area, with a strong family-business culture. Compliance paperwork, freight coordination and multilingual customer contact are all real daily loads.
We work with teams across Griffith: Griffith CBD · Yoogali · Hanwood · Yenda · Leeton.
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How we build AI demand forecasting for a Griffith team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Griffith business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usThe outcome for Griffith teams
If we build the right slice first, Griffith teams feel the difference inside the first month. Stockouts down 35%, overstock down 22% in the first season.
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
What's the realistic timeline for AI demand forecasting with a Griffith?
Most Griffith 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 demand forecasting worth it for a smaller Griffith?
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.
Anyone else in this space using AI demand forecasting?
Plenty. Stockouts down 35%, overstock down 22% in the first season. The interesting question is rarely "does it work" - it's "is your team ready to use the output." That's what we'd scope on the call.
What happens if we want to swap a vendor out later?
AI demand forecasting 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. Prophet, DuckDB, Claude, BigQuery, Vercel are our defaults, but the build is intentionally portable.
One short call.
Tell us what you're trying to fix. We'll come back inside a working day.
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.