Semantic search (RAG) in Nelson.
Built and supported here – the way a Nelson business would actually use it.
What this is
AI search over your data
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.
Average search time drops from 6 minutes to 12 seconds.
How it helps your Nelson team
What you actually get.
- Indexes Drive, SharePoint, Notion, Slack, your CRM
- Returns answers with source links – no hallucinations
- Permissioned so staff only see what they should
- Re-indexes nightly so results stay fresh
We've worked with enough operators in Nelson to know that the brief that arrives in our inbox is rarely the brief that ends up shipped. The first thing we do on any semantic search (RAG) project is sit with your team for a day before we propose anything.
What changes for Nelson teams after this lands: the work that used to need a person stays done, the work that needs a person gets done with their attention undivided. Average search time drops from 6 minutes to 12 seconds.
Why Nelson businesses are a fit for this.
Aquaculture, hops, tourism, and a deep creative sector. Nelson teams want tools that fit a smaller crew without enterprise overhead.
We work with teams in
What we build
Semantic search (RAG), tailored to Nelson businesses.
- 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
Common questions
Before you book the call.
When does semantic search (RAG) actually pay back? +
Inside the first quarter, in our experience. We pick the first slice specifically because it's the highest-leverage workflow for a Nelson – so the savings start landing before the rest of the build is finished.
Do you do hourly billing or fixed price? +
Fixed price for the pilot, every time. After that it's your call – fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.
Do you have proof this works for Nelson businesses? +
Direct case study: Average search time drops from 6 minutes to 12 seconds. Happy to walk you through full numbers on a call.
Will this run on our own infrastructure? +
Yes, where it makes sense. Semantic search (RAG) can sit entirely in your cloud account, with model calls routed through endpoints you control. We default to Pinecone, Claude, Postgres pgvector, Vercel AI SDK but the architecture supports your existing platform choices.
Skip the pitch.
Tell us the workflow and we'll come back with what we'd build first.
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