Hong Kong Island · Applied AI
AI demand forecasting in Cyberport
Built and supported here – the way a Cyberport business would actually use it.
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.
We've worked with enough operators in Cyberport 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.
- 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
Why Cyberport businesses choose this
Cyberport is Hong Kong's dedicated tech and fintech precinct, purpose-built to house the city's startup and digital economy – AI here is judged by founders and engineers, not procurement committees.
What's different about doing this work in Cyberport.
A government-backed tech park concentrating fintech, AI and digital media startups alongside venture capital and accelerator programs. Businesses here expect AI built with real technical rigor, since many of them build software themselves.
We work with teams across Cyberport: Pok Fu Lam · Telegraph Bay · Wah Fu · Aberdeen.
How we build AI demand forecasting for a Cyberport team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Cyberport business, so value lands before the build is finished. Every engagement starts with a short call and a paid discovery if the brief needs one.
AI sales + stock forecasting.
The outcome for Cyberport teams
Stockouts down 35%, overstock down 22% in the first season. For Cyberport teams, that almost always shows up as fewer interruptions and a calmer week, not a dashboard chart.
Stockouts down 35%, overstock down 22% in the first season.
AI demand forecasting in Cyberport – common questions
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 Cyberport – 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.
Can you walk us through a comparable build?
Yes – on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Stockouts down 35%, overstock down 22% in the first season.
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.
Ready to talk specifics?
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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.