Washington · Applied AI
Semantic search (RAG) in Seattle
Semantic search (RAG) designed around the way a Seattle team actually runs.
What semantic search (RAG) actually does
Search that understands intent, not just keywords. Your team types what they mean – and gets the right document, ticket, or product from across every system, with citations.
Seattle sits in a regional context that genuinely changes the build. Connectivity assumptions, the rhythm of the working week, the proximity of your team to your customers – none of those are details our default semantic search (RAG) template would catch.
- 01 Indexes Drive, SharePoint, Notion, Slack, your CRM
- 02 Returns answers with source links – no hallucinations
- 03 Permissioned so staff only see what they should
- 04 Re-indexes nightly so results stay fresh
Built on: Pinecone Claude Postgres pgvector 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.
Our field notes from Seattle builds.
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 semantic search (RAG) 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.
AI search over your data.
The outcome for Seattle teams
The shape of the result for Seattle teams: Average search time drops from 6 minutes to 12 seconds. Built on Pinecone, hardened with the rest of the stack as it scales.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Seattle – common questions
How quickly can we see something running?
Week three for a clickable internal demo against real data. Week six for a slice your team can actually use. We hold ourselves to those numbers because they're what stops a project drifting into "endless discovery".
What's the smallest engagement you'd take on?
A two-week paid discovery for Seattle 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.
Can you walk us through a comparable build?
Yes – on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Average search time drops from 6 minutes to 12 seconds.
What tools do you build semantic search (RAG) on?
For semantic search (RAG) we usually reach for Pinecone, Claude, Postgres pgvector, Vercel AI SDK. We're tool-agnostic at heart – we pick what your Seattle team can actually run after we hand the build over, not what looks good on a vendor sticker.
Sketch this with us.
We'll map your real workflow before quoting anything.
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.