Ashburton businesses don't need another generic AI pitch. AI knowledge base 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 Canterbury.
AI knowledge base that lives in your stack, not on a vendor's roadmap. Shipped from Canterbury.
Our goal is to give Ashburton businesses a three-day weekend, so people can spend more time with their families and the people they love :)
What AI knowledge base actually does
Your team's tribal knowledge, finally searchable. Upload your SOPs, training videos, past emails, and Slack threads - your team asks questions and gets answers with citations.
- 01 Ingests PDFs, Word docs, videos, Slack, Notion, Drive
- 02 Answers with citations back to source documents
- 03 Permission-aware - staff only see what they should
- 04 Detects stale docs and prompts owners to update
Built on: Claude Pinecone Vercel AI SDK Postgres MCP
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 AI knowledge base 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 Ashburton 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 AI knowledge base 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 Ashburton engagements we've shipped.
- Mid-Canterbury runs on irrigated dairy, arable, and a tight industrial sector - AI here is about turning paddock data into operational decisions.
- Dairy, arable, and a strong engineering base supporting irrigation and processing. AI lands well when it makes long days shorter.
We work with teams across Ashburton: Ashburton CBD · Hampstead · Methven · Rakaia · Tinwald.
Talk to us about this →
How we build AI knowledge base for an Ashburton team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Ashburton business, so value lands before the build is finished. Internal AI knowledge base.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Ashburton 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 Ashburton teams
We'd call the engagement a success when Ashburton teams are using the system without thinking about us. New staff get to productive 3x faster - less senior-team interruption.
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
How quickly can we see something running?
Week three for a clickable internal demo against real data. Week six for a slice your team can actually use. We hold ourselves to those numbers because they're what stops a project drifting into "endless discovery".
What's the smallest engagement you'd take on?
A two-week paid discovery for Ashburton 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.
Do you have proof this works for Ashburton businesses?
Direct case study: New staff get to productive 3x faster - less senior-team interruption. Happy to walk you through full numbers on a call.
What tools do you build AI knowledge base on?
For AI knowledge base we usually reach for Claude, Pinecone, Vercel AI SDK, Postgres, MCP. We're tool-agnostic at heart - we pick what your Ashburton team can actually run after we hand the build over, not what looks good on a vendor sticker.
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.