Central Business District · Applied AI
Semantic search (RAG) in Singapore
Semantic search (RAG) that lives in your stack, not on a vendor's roadmap. Shipped from Central Business District.
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.
Singapore 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 Singapore businesses choose this
Singapore has one of the highest AI adoption rates in the world, and the CBD runs on banking, wealth management and regional HQ operations – AI here has to meet a market that's already fluent in it.
The pattern across Singapore engagements we've shipped.
Home to the regional headquarters of most major global banks and a dense wealth management sector, sitting inside a government that's pushed AI adoption harder than almost any other country. Singapore businesses expect AI that's precise, compliant and genuinely production-ready, not a pilot.
We work with teams across Singapore: Raffles Place · Marina Bay · Shenton Way · Tanjong Pagar · City Hall.
How we build semantic search (RAG) for a Singapore team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Singapore 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 Singapore teams
The shape of the result for Singapore teams: Average search time drops from 6 minutes to 12 seconds. Built on Pinecone, hardened with the rest of the stack as it scales.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Singapore – common questions
How fast could we have semantic search (RAG) in production?
Eight to ten weeks for most Singapore 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 does semantic search (RAG) cost for a Singapore?
Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.
Can you walk us through a comparable build?
Yes – on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Average search time drops from 6 minutes to 12 seconds.
What if our Singapore 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 reply, one direction.
We don't run sequences or follow-up automation. One useful answer, one decision on your side.
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Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.