Semantic search (RAG) that lives in your stack, not on a vendor's roadmap. Shipped from Bay of Plenty.

Our goal is to give Rotorua businesses a three-day weekend, so people can spend more time with their families and the people they love :)

Rotorua 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.

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

What you actually get

Every engagement is scoped and quoted up front. This is what is in the box.

How semantic search (RAG) compares

The two things most businesses do instead, and where each one runs out.

 Hiring for itAn off-the-shelf toolKiwi Dynamics
Fit to how you workFits perfectly, costs a salaryYou bend your process to suit the toolBuilt around the workflow you already run
Time to something usefulImmediate, and permanentQuick to switch on, slow to make fitA working slice in weeks, then hardened
Who owns the dataYou doThe vendor, on the vendor's termsYou do, in your own accounts
When it breaksThat person sorts it, if they are inA support queue and a ticket numberThe people who built it
What it costsA salary, every year, foreverPer seat, forever, used or notScoped and quoted up front

Our field notes from Rotorua builds.

  • Rotorua is forestry, tourism, and a strong Māori-led economy - AI here works hardest when it respects relationships first.
  • From iwi-owned enterprises to international tour operators, Rotorua businesses want AI that handles peaks without flattening the manaakitanga.

We work with teams across Rotorua: CBD · Ngongotaha · Lake Tarawera · Reporoa · Tokoroa.

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How we build semantic search (RAG) for a Rotorua team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Rotorua business, so value lands before the build is finished. AI search over your data.

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How the work runs

The outcome for Rotorua teams

What changes for Rotorua 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.

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

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*Every engagement is scoped and quoted up front. Results vary by workflow and business.

How much is not automating costing you?

Nine hours a week of admin is 468 hours a year. With Kiwi Dynamics, that drops to about 52.

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*Based on 9 hours a week of admin at Kiwi Dynamics' typical 80% automation rate. Your number may vary, the calculator uses your own.

FAQ

What's the realistic timeline for semantic search (RAG) with a Rotorua?

Most Rotorua 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".

Is semantic search (RAG) worth it for a smaller Rotorua?

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.

Do you have proof this works for Rotorua 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.

One short call.

Tell us what you're trying to fix. We'll come back inside a working day.

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.