Most cleaning companies we talk to aren't short of dashboards or tools – they're short of an hour back in the week. That's the lens we put on AI demand forecasting: not a tech showcase, but a careful look at the one or two workflows where a cleaning company is paying for the same problem to be solved twice.
Cleaning companies · Applied AI
Cleaning companies-grade AI demand forecasting, shipped by people who've done it before.
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
From the trenches
The shape of every cleaning companie brief we've seen.
- Cleaning businesses run on recurring contracts and one-off quotes, both of which need fast, consistent follow-up to win.
- Quote turnaround and scheduling reliability are what most decide whether a cleaning company keeps a client past the first job.
How we build AI demand forecasting for cleaning companies.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a cleaning company business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usThe outcome for cleaning companies
We'd call the engagement a success when cleaning companies are using the system without thinking about us. 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.
Questions
FAQ
How fast could we have AI demand forecasting in production?
Eight to ten weeks for most cleaning companies. Faster if your data is in good shape and slower if we're untangling a legacy integration first. We'll give you a realistic number on the scoping call rather than the optimistic one.
What's the smallest engagement you'd take on?
A two-week paid discovery for cleaning companies that aren't sure whether the build is worth doing at all. You get a one-page write-up of what we'd build, what we'd skip, and what it would cost. About 30% of those discoveries end with us recommending you don't proceed.
Do you have proof this works for cleaning companies?
Direct case study: Stockouts down 35%, overstock down 22% in the first season. Happy to walk you through full numbers on a call.
What if our cleaning company doesn't have any data ready?
Most don't. Getting the data into shape – ingestion, cleaning, the lightweight contracts you need before any model is useful – is part of the engagement. For AI demand forecasting specifically, we typically run that work on Prophet, DuckDB, Claude, BigQuery, Vercel and assume messy starting conditions from day one.
Twenty minutes, your call.
You describe what's broken. We'll tell you what we'd actually do about it.
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.