East Region · Applied AI
Semantic search (RAG) in Changi
Semantic search (RAG) designed around the way a Changi 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 Changi is that the businesses here tend to be sharper about what they want than the brief lets on. Semantic search (RAG) for a Changi 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 Changi businesses choose this
Changi runs on aviation, logistics and air cargo as one of the world's busiest air hubs – AI here means keeping freight, scheduling and compliance moving at airport speed.
Our field notes from Changi builds.
Changi Airport and its surrounding air-cargo and logistics ecosystem move an enormous volume of freight and passengers on tight schedules. Businesses here want AI that handles logistics coordination and compliance documentation without becoming the bottleneck.
We work with teams across Changi: Changi Airport · Changi Business Park · Loyang · Pasir Ris · Tampines.
How we build semantic search (RAG) for a Changi team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Changi 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 Changi teams
The shape of the result for Changi 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 Changi – common questions
How fast could we have semantic search (RAG) in production?
Eight to ten weeks for most Changi 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 Changi 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.
What's the realistic outcome for Changi 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 if our Changi 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.
Get in touch
Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.