Katherine sits in a regional context that genuinely changes the build. Connectivity assumptions, the rhythm of the working week, the proximity of your team to your customers - none of those are details our default AI demand forecasting template would catch.
AI demand forecasting that lives in your stack, not on a vendor's roadmap. Shipped from Northern Territory.
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 Katherine engagements we've shipped.
- Katherine is defence, pastoral country and transport where the Stuart and Victoria highways meet.
- RAAF Base Tindal, cattle stations, horticulture and freight. Defence work brings compliance requirements, and pastoral clients are often hours from the nearest town.
We work with teams across Katherine: Katherine CBD · Katherine East · Katherine South · Tindal · Mataranka.
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How we build AI demand forecasting for a Katherine team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Katherine business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usThe outcome for Katherine teams
The shape of the result for Katherine 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
What's the realistic timeline for AI demand forecasting with a Katherine?
Most Katherine 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".
What does AI demand forecasting cost for a Katherine?
Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.
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.