AI Automation Site Property

Joris

A buyer's agent in Newcastle, starting from nothing. We first built a website that ranks in search, then the systems behind it: a custom connection that lets his own Claude and ChatGPT use our tools, and a program that assesses every lead.

Joris screenshot
Client
Buyer's agent, Newcastle
Industry
Property
Shipped
2025–26
Live at
joris.com.au
Before

New business, no reputation, and one person doing everything.

Joris helps people buy houses in Newcastle. He started with no website, no search rankings and established agents to compete with. The first job was to look established and be found.

The second job came once the leads did. A buyer's agent's work is full of admin. Sending a client a property report means generating it on a data service, pulling the details together, finding the client's record and writing the email. Enquiries arrive as Instagram messages, Facebook messages and website forms, and each one has to be read, judged and logged before anyone calls back. Footage from inspections piles up on a phone, never posted. He did all of it himself, after hours.

What we built · 01

A brand-new website that ranks in search.

The site is built with SvelteKit, with every page generated in advance, hosted on Cloudflare and edited in CloudCannon, a visual editor. It loads in under half a second, with perfect performance scores. Service pages, success stories and resources target the searches Newcastle buyers actually make, and every page carries structured data (labels for search engines). Google noticed, and so did the people searching for a buyer's agent. The enquiries that followed are what made the next two systems worth building.

The Joris homepage
The Joris buyer's agent service page
What we built · 02

His own AI assistant, with our tools.

Joris already pays for Claude and ChatGPT. Rather than give him another app to log into, we built a custom MCP server, a connection that lets an AI assistant use tools we write, and he installed it in both. He asks in a normal conversation, and our tools do the work behind it.

"Send Sarah the report on 12 Smith Street" is now one sentence. Our tools generate the report on the property data service, fetch every detail back, look Sarah up in HubSpot, where he keeps his contacts, build a formatted report and email it to her, then tell him it is done. Because we host the server, adding a new ability means switching a tool on, with no update for him to install and no new screen to learn.

What we built · 03

Every lead read, judged and filed.

A small program on Cloudflare receives every message from his social accounts and every enquiry from the website. Claude reads and assesses each one: a buyer with a budget and a suburb is not the same as an agent selling leads. It then creates or updates the contact in HubSpot, moves it to the right stage of his sales pipeline and creates a to-do for Joris, with the context already written. He opens HubSpot to a list of people worth calling, not an inbox to sort through.

In the oven · 04

From inspection videos to social posts.

The next system is in progress. Joris drops the day's inspection footage into a folder. The AI goes through it, picks the moments, cuts them into short clips, writes the captions and posts them to his social accounts on a schedule. As with everything we build for him, he approves the first posts, and the system only works on its own once it has a good track record.

What changed

The numbers.

  • <0.5s

    page loads

    perfect performance scores at launch, on a brand-new domain

  • 1

    sentence to send a property report

    asked in his own Claude or ChatGPT, sent by our tools

  • 24/7

    leads assessed

    every social message and web enquiry, filed in HubSpot with a to-do

  • 0

    new logins for Joris

    everything runs through the assistant he already pays for

Lead and report counts are not published; the systems are measured on the managed plan's monthly report.

Runs on

No lock-in.

The site, the MCP server and the lead program run in Joris's own Cloudflare account, alongside the HubSpot account he already had. Every action the systems take is logged, and the AI assistants can only use the tools we choose to give them.

  • SvelteKit

    the site, pre-rendered

  • CloudCannon

    editing the site without us

  • Cloudflare Workers

    the MCP server and the lead program

  • MCP

    the standard his Claude and ChatGPT use to reach our tools

  • HubSpot

    his contacts, sales pipeline and to-dos

  • Claude

    reads and assesses every lead and writes the context

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