When a landscaping business asks us about AI demand forecasting, the first question we put back is the same one every time: which part of your week, if it disappeared, would change how you feel on a Friday? We start the build from that answer.
The version of AI demand forecasting that landscaping businesses still use a year after launch.
Our goal is to give landscaping 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 landscaping 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 hearing from NZ landscaping businesses.
- Landscapers quote big jobs from a site visit and small jobs from a phone call, and both processes usually run on paper.
- Seasonal demand and slow quoting are the two things that most limit how much work a landscaping business can take on.
How we build AI demand forecasting for landscaping businesses.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a landscaping business 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 landscaping 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 landscaping businesses
What changes for landscaping businesses after this lands: the work that used to need a person stays done, the work that needs a person gets done with their attention undivided. 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
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 landscaping business - 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.
Do you have proof this works for landscaping businesses?
Direct case study: Stockouts down 35%, overstock down 22% in the first season. Happy to walk you through full numbers on a 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.
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.