California · Applied AI
Semantic search (RAG) in San Jose
Semantic search (RAG) designed around the way a San Jose 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.
The reason we take on work in San Jose is that the businesses here tend to be sharper about what they want than the brief lets on. Semantic search (RAG) for a San Jose team almost always ends up looking different to semantic search (RAG) for a downtown Auckland one.
- 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 Jose businesses choose this
San Jose sits in the middle of Silicon Valley, where the AI bar is set by the companies inventing it – AI here has to be genuinely production-grade, not a wrapper on a public API.
What we keep seeing in San Jose.
The commercial and cultural centre of Silicon Valley, surrounded by the companies that build the models everyone else uses. San Jose businesses expect AI built with real engineering discipline, because they can tell when it isn't.
We work with teams across San Jose: Downtown San Jose · Santana Row · Willow Glen · North San Jose · Almaden Valley · Berryessa.
How we build semantic search (RAG) for a San Jose team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your San Jose 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 Jose teams
Average search time drops from 6 minutes to 12 seconds. For San Jose teams, that almost always shows up as fewer interruptions and a calmer week, not a dashboard chart.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in San Jose – common questions
What's the realistic timeline for semantic search (RAG) with a San Jose?
Most San Jose businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational – your team gets to use the thing well before the engagement is "done".
What does semantic search (RAG) cost for a San Jose?
Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.
Do you have proof this works for San Jose businesses?
Direct case study: Average search time drops from 6 minutes to 12 seconds. Happy to walk you through full numbers on a call.
What happens if we want to swap a vendor out later?
Semantic search (RAG) is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Pinecone, Claude, Postgres pgvector, Vercel AI SDK are our defaults, but the build is intentionally portable.
One reply, one direction.
We don't run sequences or follow-up automation. One useful answer, one decision on your side.
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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.