The version of AI demand forecasting that works for a San Jose 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 San Jose.
Our goal is to give San Jose 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 San Jose 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 San Jose context, plainly.
- San Jose sits in the middle of Silicon Valley, where the AI bar is set by the companies inventing it - AI here has to be genuinely production-grade, not a wrapper on a public API.
- The commercial and cultural centre of Silicon Valley, surrounded by the companies that build the models everyone else uses. San Jose businesses expect AI built with real engineering discipline, because they can tell when it isn't.
We work with teams across San Jose: Downtown San Jose · Santana Row · Willow Glen · North San Jose · Almaden Valley · Berryessa.
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How we build AI demand forecasting for a San Jose team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your San Jose 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 San Jose 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 San Jose teams
If we build the right slice first, San Jose 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
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 San Jose - so the savings start landing before the rest of the build is finished.
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 San Jose 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.
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.
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.