Most of our Chicago engagements start the same way: a 20-minute call where the owner describes a workflow we've heard before in shape but never in detail. AI demand forecasting is then designed against the detail, not the shape.
For Chicago operators who want a working pilot in weeks, not a year-long programme.
Our goal is to give Chicago 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 Chicago 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 |
What's different about doing this work in Chicago.
- Chicago runs on logistics, manufacturing, finance and a genuinely diverse industrial base - AI here means fitting into operations that were already running lean before AI existed.
- A major freight and logistics hub, a deep manufacturing base, and a futures and options trading industry with zero patience for anything slow. Chicago businesses want AI that respects an operation that already runs tight.
We work with teams across Chicago: The Loop · River North · Wicker Park · Lincoln Park · Evanston · Naperville.
Talk to us about this →
How we build AI demand forecasting for a Chicago team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Chicago 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 Chicago 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 Chicago teams
The shape of the result for Chicago 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
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 Chicago - 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 Chicago businesses are surprised how small the first cheque is.
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.
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.