Washington · Applied AI
AI data analytics in Seattle
Built and supported here – the way a Seattle business would actually use it.
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.
Most of our Seattle 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 data analytics is then designed against the detail, not the shape.
- 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
Why Seattle businesses choose this
Seattle runs on cloud computing, aerospace and coffee, home to the companies whose infrastructure half the internet's AI runs on – AI here means holding up to serious technical scrutiny.
The Seattle context, plainly.
Two of the world's largest cloud providers are headquartered here alongside a major aerospace manufacturing base. Seattle businesses, even small ones, tend to have someone on staff who can and will check your work.
We work with teams across Seattle: Downtown Seattle · Capitol Hill · Bellevue · Fremont · Ballard · Redmond.
How we build AI data analytics for a Seattle team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Seattle business, so value lands before the build is finished. Every engagement starts with a short call and a paid discovery if the brief needs one.
Ask your data in English.
The outcome for Seattle teams
If we build the right slice first, Seattle teams feel the difference inside the first month. Owners check the business in 2 minutes instead of 2 hours.
Owners check the business in 2 minutes instead of 2 hours.
AI data analytics in Seattle – common questions
What's a typical engagement length for Seattle businesses?
Six to twelve weeks for the build, then a short managed-services month while the system goes from "shipped" to "owned by your team". After that you keep us on retainer if you want, or take it from there yourself.
Are there hidden costs we should plan for?
Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.
What's the realistic outcome for Seattle 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.
Can you work with our existing systems?
Yes. The default AI data analytics stack we reach for is DuckDB, Claude, Metabase, BigQuery, Vercel AI SDK, but we'll bend it around whatever you already run – Xero, HubSpot, Shopify, Cin7, your own in-house apps. The discovery week maps every data source before any build starts.
The honest version of a sales call.
No deck. No discovery doc. Just whether this is worth building and what it would cost.
Get in touch
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.