AI chatbots 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 :)

Whanganui businesses don't need another generic AI pitch. AI chatbots only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Manawatū-Whanganui.

What AI chatbots actually does

Trained on your business, not Wikipedia. AI assistants that answer pricing, hours, availability, bookings - and escalate cleanly when the customer needs a human.

  • 01 Trained on your documents, FAQs, and past tickets
  • 02 Books appointments, takes deposits, qualifies leads
  • 03 Live hand-off to a human when needed
  • 04 Daily quality reports so you stay in control

Built on: Claude OpenAI Custom RAG pipelines

What you actually get

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

How AI chatbots 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

What we keep seeing in Whanganui.

  • 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 AI chatbots 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 customer support.

Talk to us

How the work runs

The outcome for Whanganui teams

The shape of the result for Whanganui teams: Handles 60-70% of routine enquiries without human input. Built on Claude, hardened with the rest of the stack as it scales.

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

Start a conversation

*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.

Try the calculator

*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 AI chatbots 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 AI chatbots 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.

What's the realistic outcome for Whanganui businesses?

Handles 60-70% of routine enquiries without human input. We don't promise tenfold lifts because we don't see them outside of marketing decks.

What happens if we want to swap a vendor out later?

AI chatbots 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. Claude, OpenAI, Custom RAG pipelines 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.