California · Applied AI
Semantic search (RAG) in San Francisco
Built and supported here – the way a San Francisco business would actually use it.
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.
Most of our San Francisco 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. Semantic search (RAG) is then designed against the detail, not the shape.
- 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 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.
Where San Francisco operators actually lose hours.
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 semantic search (RAG) 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.
AI search over your data.
The outcome for San Francisco teams
The shape of the result for San Francisco 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 San Francisco – common questions
How long does semantic search (RAG) take to ship for San Francisco businesses?
We aim for a working pilot inside 4-6 weeks – narrow scope, real San Francisco businesses data, measurable outcome. From there it's another 6-8 weeks of hardening before you'd consider it production. Full rollouts (multiple sites, multiple teams) typically land in 3-4 months.
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.
Anyone else in this space using semantic search (RAG)?
Plenty. Average search time drops from 6 minutes to 12 seconds. 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.
Who owns the code and the model setup?
You do, on delivery. We deploy semantic search (RAG) into your own cloud account where possible, with the model setup, prompts, evals and integration code all checked into a repo you own. Pinecone sits in your account too – we don't operate it from ours.
Worth a conversation?
Even if you don't end up working with us, you'll leave the call knowing what's worth building.
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.