AI demand forecasting that lives in your stack, not on a vendor's roadmap. Shipped from California.

Our goal is to give Fresno businesses a three-day weekend, so people can spend more time with their families and the people they love :)

The reason we take on work in Fresno is that the businesses here tend to be sharper about what they want than the brief lets on. AI demand forecasting for a Fresno team almost always ends up looking different to AI demand forecasting for a downtown Auckland one.

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

What you actually get

Every engagement is scoped and quoted up front. This is what is in the box.

How AI demand forecasting compares

The two things most businesses do instead, and where each one runs out.

 Hiring for itAn off-the-shelf toolKiwi Dynamics
Fit to how you workFits perfectly, costs a salaryYou bend your process to suit the toolBuilt around the workflow you already run
Time to something usefulImmediate, and permanentQuick to switch on, slow to make fitA working slice in weeks, then hardened
Who owns the dataYou doThe vendor, on the vendor's termsYou do, in your own accounts
When it breaksThat person sorts it, if they are inA support queue and a ticket numberThe people who built it
What it costsA salary, every year, foreverPer seat, forever, used or notScoped and quoted up front

Our field notes from Fresno builds.

  • Fresno is the commercial hub of the Central Valley, the most productive agricultural region in the country - AI here means turning harvest and logistics data into decisions.
  • Almonds, grapes and a huge share of the nation's produce move through Fresno's agribusiness and packing economy. Fresno businesses want AI that handles forecasting, logistics and seasonal staffing without a Silicon Valley budget.

We work with teams across Fresno: Downtown Fresno · Tower District · Clovis · Fig Garden · Woodward Park · Sunnyside.

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How we build AI demand forecasting for a Fresno team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Fresno business, so value lands before the build is finished. AI sales + stock forecasting.

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How the work runs

The outcome for Fresno teams

The shape of the result for Fresno 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

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*Every engagement is scoped and quoted up front. Results vary by workflow and business.

How much is not automating costing you?

Nine hours a week of admin is 468 hours a year. With Kiwi Dynamics, that drops to about 52.

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*Based on 9 hours a week of admin at Kiwi Dynamics' typical 80% automation rate. Your number may vary, the calculator uses your own.

FAQ

How fast could we have AI demand forecasting in production?

Eight to ten weeks for most Fresno businesses. Faster if your data is in good shape and slower if we're untangling a legacy integration first. We'll give you a realistic number on the scoping call rather than the optimistic one.

What's the smallest engagement you'd take on?

A two-week paid discovery for Fresno businesses that aren't sure whether the build is worth doing at all. You get a one-page write-up of what we'd build, what we'd skip, and what it would cost. About 30% of those discoveries end with us recommending you don't proceed.

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.

What if our Fresno doesn't have any data ready?

Most don't. Getting the data into shape - ingestion, cleaning, the lightweight contracts you need before any model is useful - is part of the engagement. For AI demand forecasting specifically, we typically run that work on Prophet, DuckDB, Claude, BigQuery, Vercel and assume messy starting conditions from day one.

Twenty minutes, your call.

You describe what's broken. We'll tell you what we'd actually do about it.

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Tell us what you're trying to do and we'll reply with how we'd build it - no obligation.