California · Applied AI
AI data analytics in San Francisco
AI data analytics that lives in your stack, not on a vendor's roadmap. Shipped from California.
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.
The reason we take on work in San Francisco is that the businesses here tend to be sharper about what they want than the brief lets on. AI data analytics for a San Francisco team almost always ends up looking different to AI data analytics for a downtown Auckland one.
- 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 San Francisco businesses choose this
San Francisco is where the AI industry itself is headquartered – any AI pitched here is judged against the frontier labs a few blocks away, not against a competitor's landing page.
Our field notes from San Francisco builds.
The highest concentration of AI research labs and startups anywhere in the world, sitting alongside legacy finance and professional services firms adopting AI for the first time. San Francisco is the least forgiving market for a weak AI product, and the best one for a genuinely strong one.
We work with teams across San Francisco: Financial District · SoMa · Mission District · Marina · Nob Hill · Hayes Valley.
How we build AI data analytics for a San Francisco team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your San Francisco 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 San Francisco teams
The shape of the result for San Francisco 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.
Owners check the business in 2 minutes instead of 2 hours.
AI data analytics in San Francisco – common questions
How fast could we have AI data analytics in production?
Eight to ten weeks for most San Francisco 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 San Francisco 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 data analytics?
Plenty. Owners check the business in 2 minutes instead of 2 hours. 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 San Francisco 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.
One reply, one direction.
We don't run sequences or follow-up automation. One useful answer, one decision on your side.
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.