Semantic search (RAG) in Blenheim.
AI search over your data – wired into a Blenheim workflow, not bolted on the side.
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 Blenheim 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
Blenheim businesses don't need another generic AI pitch. Semantic search (RAG) only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Marlborough.
If we build the right slice first, Blenheim teams feel the difference inside the first month. Average search time drops from 6 minutes to 12 seconds.
What we keep seeing in Blenheim.
Vineyards, cellar doors, mussel farms, and a thriving cycle-trail tourism economy. AI here means forecasting the season and never missing a booking.
We work with teams in
What we build
Semantic search (RAG), tailored to Blenheim 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.
How fast could we have semantic search (RAG) in production? +
Eight to ten weeks for most Blenheim 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 Blenheim 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 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.
What if our Blenheim 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 semantic search (RAG) specifically, we typically run that work on Pinecone, Claude, Postgres pgvector, Vercel AI SDK and assume messy starting conditions from day one.
One short call.
Tell us what you're trying to fix. We'll come back inside a working day.
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