Whanganui 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.
Semantic search (RAG) that lives in your stack, not on a vendor's roadmap. Shipped from Manawatū-Whanganui.
Our goal is to give Whanganui businesses a three-day weekend, so people can spend more time with their families and the people they love :)
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.
- 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
Without Kiwi Dynamics
With Kiwi Dynamics
What you actually get
Every engagement is scoped and quoted up front. This is what is in the box.
- A working pilot, in productionNot a prototype on someone's laptop. The first slice of semantic search (RAG) runs against real work within weeks.
- Your data stays yoursIt runs on your accounts and your tools. If we part ways you keep the system and everything in it.
- The workflow mapped before codeWe write down what good looks like for Whanganui businesses first, so nobody is guessing at handover.
- Support after it landsThe people who built it stay reachable when the business changes shape around it.
How semantic search (RAG) compares
The two things most businesses do instead, and where each one runs out.
| Hiring for it | An off-the-shelf tool | Kiwi Dynamics | |
|---|---|---|---|
| Fit to how you work | Fits perfectly, costs a salary | You bend your process to suit the tool | Built around the workflow you already run |
| Time to something useful | Immediate, and permanent | Quick to switch on, slow to make fit | A working slice in weeks, then hardened |
| Who owns the data | You do | The vendor, on the vendor's terms | You do, in your own accounts |
| When it breaks | That person sorts it, if they are in | A support queue and a ticket number | The people who built it |
| What it costs | A salary, every year, forever | Per seat, forever, used or not | Scoped and quoted up front |
The pattern across Whanganui engagements we've shipped.
- Whanganui blends creative scene, river-led tourism, and steady local trades - AI here helps small operators run lighter and reach further.
- Tourism, creative industries, and a growing remote-work population. AI tools that scale a small team's reach without enterprise overhead win.
We work with teams across Whanganui: Whanganui CBD · Castlecliff · Aramoho · Marton · Bulls.
Talk to us about this →
How we build semantic search (RAG) for a Whanganui team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Whanganui business, so value lands before the build is finished. AI search over your data.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Whanganui businesses the most hours or the most leads, and deliberately ignore the rest for now.
- Ship a working sliceA narrow version goes into production in weeks, against real work, so the value shows up before the build is finished.
- Prove it, then widenWe measure it against what the work cost before. If it does not pay for itself, we say so rather than scaling it.
- Harden and hand overLogging, fallbacks and a real handover, so it keeps running when we are not in the room.
The outcome for Whanganui teams
We'd call the engagement a success when Whanganui teams are using the system without thinking about us. Average search time drops from 6 minutes to 12 seconds.
Not your typical AI agency.
Honest about what AI can and cannot do
Ships the one workflow that pays for itself
Hours given back, never the size of the invoice
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
FAQ
What's the realistic timeline for semantic search (RAG) with a Whanganui?
Most Whanganui businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational - your team gets to use the thing well before the engagement is "done".
What does semantic search (RAG) cost for a Whanganui?
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.
Do you have proof this works for Whanganui businesses?
Direct case study: Average search time drops from 6 minutes to 12 seconds. Happy to walk you through full numbers on a call.
What happens if we want to swap a vendor out later?
Semantic search (RAG) is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Pinecone, Claude, Postgres pgvector, Vercel AI SDK are our defaults, but the build is intentionally portable.
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.