There is a version of AI demand forecasting that real estate agencies buy off a shelf and quietly stop using inside a month. Then there's the version that's wired into your real workflow, owned by a person on your team, and still in use a year later. We only build the second one.
Real estate agencies · Applied AI
AI sales + stock forecasting, built around how a real estate agency actually works.
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
The honest read
Where most real estate agencies engagements actually deliver value.
- Agents live on their phones between open homes, listing enquiries, and vendor updates that can't wait until Monday.
- Lead response speed decides who gets the listing, and most agencies lose leads simply because nobody replied fast enough.
How we build AI demand forecasting for real estate agencies.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a real estate agency business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usThe outcome for real estate agencies
Stockouts down 35%, overstock down 22% in the first season. For real estate agencies, that almost always shows up as fewer interruptions and a calmer week, not a dashboard chart.
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.
Questions
FAQ
What's a typical engagement length for real estate agencies?
Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.
Are there hidden costs we should plan for?
Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.
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.
Can you work with our existing systems?
Yes. The default AI demand forecasting stack we reach for is Prophet, DuckDB, Claude, BigQuery, Vercel, but we'll bend it around whatever you already run – Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.
The honest version of a sales call.
No deck. No discovery doc. Just whether this is worth building and what it would cost.
Get in touch
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.