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

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

Tacoma businesses don't need another generic AI pitch. AI demand forecasting only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Washington.

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

What Tacoma teams tell us when they get on a call.

  • Tacoma runs on port logistics, manufacturing and military, a genuinely industrial counterpart to Seattle up the freeway - AI here means operational reliability over polish.
  • One of the busiest container ports on the West Coast, a major military presence at Joint Base Lewis-McChord, and a manufacturing base that never left. Tacoma businesses want AI that works the same way every shift, not a flashy demo.

We work with teams across Tacoma: Downtown Tacoma · Point Ruston · Hilltop · Proctor District · Lakewood · Puyallup.

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How we build AI demand forecasting for a Tacoma team.

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

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How the work runs

The outcome for Tacoma teams

If we build the right slice first, Tacoma 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

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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

What's the realistic timeline for AI demand forecasting with a Tacoma?

Most Tacoma businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational - your team gets to use the thing well before the engagement is "done".

What does AI demand forecasting cost for a Tacoma?

Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.

What's the realistic outcome for Tacoma businesses?

Stockouts down 35%, overstock down 22% in the first season. We don't promise tenfold lifts because we don't see them outside of marketing decks.

What happens if we want to swap a vendor out later?

AI demand forecasting is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Prophet, DuckDB, Claude, BigQuery, Vercel are our defaults, but the build is intentionally portable.

Sketch this with us.

We'll map your real workflow before quoting anything.

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.