Hong Kong Island · Applied AI
Semantic search (RAG) in Central
Built and supported here – the way a Central business would actually use it.
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.
Most of our Central engagements start the same way: a 20-minute call where the owner describes a workflow we've heard before in shape but never in detail. Semantic search (RAG) is then designed against the detail, not the shape.
- 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 Central businesses choose this
Central is Hong Kong's financial core, home to the regional headquarters of most major global banks – AI here has to meet a market built on precision, compliance and speed of execution.
The Central context, plainly.
Hong Kong's stock exchange, the regional HQs of the world's biggest banks, and a dense wealth management and private banking sector all sit within a few blocks. Businesses here expect AI that's compliant-by-default and genuinely production-ready, not a pilot.
We work with teams across Central: Admiralty · Sheung Wan · IFC · Mid-Levels · Wan Chai.
How we build semantic search (RAG) for a Central team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Central 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 Central teams
What changes for Central 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.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Central – common questions
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 Central – 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 Central 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.
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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.