New South Wales · Applied AI
Semantic search (RAG) in Wollongong
Semantic search (RAG) designed around the way a Wollongong 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 Wollongong is that the businesses here tend to be sharper about what they want than the brief lets on. Semantic search (RAG) for a Wollongong 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 Wollongong businesses choose this
Wollongong runs on steel, education and a growing tech and services sector an hour south of Sydney – AI here competes for attention against Sydney rates on a regional budget.
What Wollongong teams tell us when they get on a call.
The Port Kembla steelworks, a major university, and a growing number of Sydney-priced professional services firms relocating for cheaper rent. Wollongong teams want AI that punches above a regional budget.
We work with teams across Wollongong: Wollongong CBD · Port Kembla · Shellharbour · Dapto · Fairy Meadow · Corrimal.
How we build semantic search (RAG) for a Wollongong team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Wollongong 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 Wollongong teams
We'd call the engagement a success when Wollongong teams are using the system without thinking about us. Average search time drops from 6 minutes to 12 seconds.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Wollongong – 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".
Is semantic search (RAG) worth it for a smaller Wollongong?
Often, yes – and counterintuitively the ROI is sometimes faster than for the big end of town because there's less integration overhead. We'll tell you honestly on the scoping call if it isn't.
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 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 Wollongong 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.
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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.