Ashburton 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 AI agents template would catch.
Canterbury · Applied AI
AI agents that lives in your stack, not on a vendor's roadmap. Shipped from Canterbury.
What AI agents actually does
Autonomous AI agents that don't just answer – they get things done. They check inventory, draft proposals, file paperwork, and chase quotes while your team focuses on the human work.
- 01 Goal-driven agents that complete multi-step tasks
- 02 Connect to your tools – Xero, HubSpot, Gmail, Slack, your CRM
- 03 Human-in-the-loop checkpoints for anything risky
- 04 Full audit log of every action the agent takes
Built on: Claude Agent SDK OpenAI Agents LangGraph n8n MCP
Without Kiwi Dynamics
With Kiwi Dynamics
Canterbury
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.
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How we build AI agents for a 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. AI agents that do work.
Talk to usThe outcome for Ashburton teams
The shape of the result for Ashburton teams: Replaces 15+ hours of weekly back-office work per agent deployed. Built on Claude Agent SDK, 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
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
Questions
FAQ
How fast could we have AI agents in production?
Eight to ten weeks for most Ashburton 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 does AI agents cost for a Ashburton?
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.
Anyone else in this space using AI agents?
Plenty. Replaces 15+ hours of weekly back-office work per agent deployed. The interesting question is rarely "does it work" – it's "is your team ready to use the output." That's what we'd scope on the call.
What if our Ashburton 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 AI agents specifically, we typically run that work on Claude Agent SDK, OpenAI Agents, LangGraph, n8n, MCP 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.