MCP integrations in Wānaka.
MCP integrations designed around the way a Wānaka team actually runs.
What this is
Connect Claude to your stack
Wire Claude, ChatGPT, or Gemini directly into your tools with the Model Context Protocol. Your team uses AI in their existing inbox, CRM, or chat – with your data, your permissions, your guardrails.
Existing AI tools become 10x more useful with real business context.
How it helps your Wānaka team
What you actually get.
- Custom MCP servers for your CRM, ERP, or in-house tools
- Permission-aware so AI only sees what each user can
- Tool-call audit log for compliance
- Works with Claude Desktop, ChatGPT, Cursor, and more
Wānaka 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 MCP integrations template would catch.
The shape of the result for Wānaka teams: Existing AI tools become 10x more useful with real business context. Built on MCP, hardened with the rest of the stack as it scales.
Our field notes from Wānaka builds.
Tourism, hospitality, lifestyle property, and trades feeding constant builds. AI shines when it lets a small operator look like a big one.
We work with teams in
What we build
MCP integrations, tailored to Wānaka businesses.
- 01 Custom MCP servers for your CRM, ERP, or in-house tools
- 02 Permission-aware so AI only sees what each user can
- 03 Tool-call audit log for compliance
- 04 Works with Claude Desktop, ChatGPT, Cursor, and more
Common questions
Before you book the call.
What's the realistic timeline for MCP integrations with a Wānaka? +
Most Wānaka 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 MCP integrations worth it for a smaller Wānaka? +
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.
Anyone else in this space using MCP integrations? +
Plenty. Existing AI tools become 10x more useful with real business context. 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 happens if we want to swap a vendor out later? +
MCP integrations 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. MCP, Claude, TypeScript, Vercel are our defaults, but the build is intentionally portable.