We've worked with enough operators in Singapore to know that the brief that arrives in our inbox is rarely the brief that ends up shipped. The first thing we do on any AI demand forecasting project is sit with your team for a day before we propose anything.
AI sales + stock forecasting, built for businesses operating in Singapore.
Our goal is to give Singapore 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 Singapore 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 |
Why Singapore businesses are a fit for this.
- Singapore has one of the highest AI adoption rates in the world, and the CBD runs on banking, wealth management and regional HQ operations - AI here has to meet a market that's already fluent in it.
- Home to the regional headquarters of most major global banks and a dense wealth management sector, sitting inside a government that's pushed AI adoption harder than almost any other country. Singapore businesses expect AI that's precise, compliant and genuinely production-ready, not a pilot.
We work with teams across Singapore: Raffles Place · Marina Bay · Shenton Way · Tanjong Pagar · City Hall.
Talk to us about this →
How we build AI demand forecasting for a Singapore team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Singapore 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 Singapore 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 Singapore teams
The shape of the result for Singapore teams: Stockouts down 35%, overstock down 22% in the first season. Built on Prophet, hardened with the rest of the stack as it scales.
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 Singapore businesses?
We aim for a working pilot inside 4-6 weeks - narrow scope, real Singapore 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.
How do you price AI demand forecasting engagements?
Fixed-scope pilots first, then either project pricing or a small monthly retainer for the ongoing work. No long lock-ins, no 18-month black-box deals. Most Singapore businesses are surprised how small the first cheque is.
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.
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.
Skip the pitch.
Tell us the workflow and we'll come back with what we'd build first.
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.