AI demand forecasting in Blenheim.
Built and supported here – the way a Blenheim business would actually use it.
What this is
AI sales + stock forecasting
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.
Stockouts down 35%, overstock down 22% in the first season.
How it helps your Blenheim team
What you actually get.
- Combines your sales history with weather, calendar, and event data
- Per-SKU and per-store forecasts, not whole-business averages
- Re-forecasts weekly as new data comes in
- Explains the why behind every number
Most of our Blenheim engagements start the same way: a 20-minute call where the owner describes a workflow we've heard before in shape but never in detail. AI demand forecasting is then designed against the detail, not the shape.
If we build the right slice first, Blenheim teams feel the difference inside the first month. Stockouts down 35%, overstock down 22% in the first season.
The Blenheim context, plainly.
Vineyards, cellar doors, mussel farms, and a thriving cycle-trail tourism economy. AI here means forecasting the season and never missing a booking.
We work with teams in
What we build
AI demand forecasting, tailored to Blenheim businesses.
- 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
Common questions
Before you book the call.
When does AI demand forecasting actually pay back? +
Inside the first quarter, in our experience. We pick the first slice specifically because it's the highest-leverage workflow for a Blenheim – so the savings start landing before the rest of the build is finished.
Do you do hourly billing or fixed price? +
Fixed price for the pilot, every time. After that it's your call – fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.
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.
Will this run on our own infrastructure? +
Yes, where it makes sense. AI demand forecasting can sit entirely in your cloud account, with model calls routed through endpoints you control. We default to Prophet, DuckDB, Claude, BigQuery, Vercel but the architecture supports your existing platform choices.
The honest version of a sales call.
No deck. No discovery doc. Just whether this is worth building and what it would cost.
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