The version of AI demand forecasting that works for a Changi business is rarely the version a national vendor would sell you. We build the one that fits how your team actually operates - usually with fewer parts than the off-the-shelf pitch.
AI sales + stock forecasting, built for businesses operating in Changi.
Our goal is to give Changi 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 Changi 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 |
The Changi context, plainly.
- Changi runs on aviation, logistics and air cargo as one of the world's busiest air hubs - AI here means keeping freight, scheduling and compliance moving at airport speed.
- Changi Airport and its surrounding air-cargo and logistics ecosystem move an enormous volume of freight and passengers on tight schedules. Businesses here want AI that handles logistics coordination and compliance documentation without becoming the bottleneck.
We work with teams across Changi: Changi Airport · Changi Business Park · Loyang · Pasir Ris · Tampines.
Talk to us about this →
How we build AI demand forecasting for a Changi team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Changi 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 Changi 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 Changi teams
If we build the right slice first, Changi teams feel the difference inside the first month. 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
How long does AI demand forecasting take to ship for Changi businesses?
We aim for a working pilot inside 4-6 weeks - narrow scope, real Changi businesses data, measurable outcome. From there it's another 6-8 weeks of hardening before you'd consider it production. Full rollouts (multiple sites, multiple teams) typically land in 3-4 months.
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.
What's the realistic outcome for Changi businesses?
Stockouts down 35%, overstock down 22% in the first season. We don't promise tenfold lifts because we don't see them outside of marketing decks.
Who owns the code and the model setup?
You do, on delivery. We deploy AI demand forecasting into your own cloud account where possible, with the model setup, prompts, evals and integration code all checked into a repo you own. Prophet sits in your account too - we don't operate it from ours.
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.