The reason we take on work in Tacoma is that the businesses here tend to be sharper about what they want than the brief lets on. AI data analytics for a Tacoma team almost always ends up looking different to AI data analytics for a downtown Auckland one.
AI data analytics designed around the way a Tacoma team actually runs.
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 :)
What AI data analytics actually does
Stop digging through dashboards. Ask plain-English questions of your sales, jobs, and customer data - get charts, summaries, and the why behind the numbers in seconds.
- 01 Natural-language queries over your Xero, Shopify, CRM data
- 02 Weekly auto-summaries delivered to inbox or Slack
- 03 Anomaly detection - flags weird weeks before you notice
- 04 Forecasts that explain themselves, not black boxes
Built on: DuckDB Claude Metabase BigQuery Vercel AI SDK
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 data analytics 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 Tacoma 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 data analytics 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 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.
Talk to us about this →
How we build AI data analytics 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. Ask your data in English.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Tacoma 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 Tacoma teams
The shape of the result for Tacoma teams: Owners check the business in 2 minutes instead of 2 hours. Built on DuckDB, 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
How fast could we have AI data analytics in production?
Eight to ten weeks for most Tacoma 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 Tacoma 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.
What's the realistic outcome for Tacoma businesses?
Owners check the business in 2 minutes instead of 2 hours. We don't promise tenfold lifts because we don't see them outside of marketing decks.
What if our Tacoma 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 data analytics specifically, we typically run that work on DuckDB, Claude, Metabase, BigQuery, Vercel AI SDK and assume messy starting conditions from day one.
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.