Every real estate agency business we've worked with has a different definition of "broken". AI agents only earns its keep when it solves the specific definition you'd give it on a bad day - which is why our first call is mostly listening.
For real estate agencies who want results in weeks, not a year-long transformation programme.
Our goal is to give real estate agencies a three-day weekend, so people can spend more time with their families and the people they love :)
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
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 agents 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 real estate agencies 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 agents 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 most real estate agencies we work with.
- Agents live on their phones between open homes, listing enquiries, and vendor updates that can't wait until Monday.
- Lead response speed decides who gets the listing, and most agencies lose leads simply because nobody replied fast enough.
How we build AI agents for real estate agencies.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a real estate agency business, so value lands before the build is finished. AI agents that do work.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing real estate agencies 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 real estate agencies
If we build the right slice first, real estate agencies feel the difference inside the first month. Replaces 15+ hours of weekly back-office work per agent deployed.
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 real estate agencies 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.
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 tools do you build AI agents on?
For AI agents we usually reach for Claude Agent SDK, OpenAI Agents, LangGraph, n8n, MCP. We're tool-agnostic at heart - we pick what your real estate agency 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.