Hong Kong Island · Applied AI
Semantic search (RAG) in Cyberport
Semantic search (RAG) that lives in your stack, not on a vendor's roadmap. Shipped from Hong Kong Island.
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.
Cyberport sits in a regional context that genuinely changes the build. Connectivity assumptions, the rhythm of the working week, the proximity of your team to your customers – none of those are details our default semantic search (RAG) template would catch.
- 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 Cyberport businesses choose this
Cyberport is Hong Kong's dedicated tech and fintech precinct, purpose-built to house the city's startup and digital economy – AI here is judged by founders and engineers, not procurement committees.
The pattern across Cyberport engagements we've shipped.
A government-backed tech park concentrating fintech, AI and digital media startups alongside venture capital and accelerator programs. Businesses here expect AI built with real technical rigor, since many of them build software themselves.
We work with teams across Cyberport: Pok Fu Lam · Telegraph Bay · Wah Fu · Aberdeen.
How we build semantic search (RAG) for a Cyberport team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Cyberport 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 Cyberport teams
We'd call the engagement a success when Cyberport 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 Cyberport – 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 Cyberport?
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.
What's the realistic outcome for Cyberport businesses?
Average search time drops from 6 minutes to 12 seconds. We don't promise tenfold lifts because we don't see them outside of marketing decks.
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 Cyberport 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.