Every cleaning company business we've worked with has a different definition of "broken". AI document processing 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.
Cleaning companies-grade AI document processing, shipped by people who've done it before.
Our goal is to give cleaning companies a three-day weekend, so people can spend more time with their families and the people they love :)
What AI document processing actually does
Scanned invoices, signed contracts, handwritten timesheets, PDF specs - extracted into clean data and routed where they belong. No more typing what someone already wrote.
- 01 Vision models read scans, photos, and handwriting
- 02 Pushes structured data into Xero, your CRM, or sheets
- 03 Confidence flags route low-certainty docs to a human
- 04 Handles te reo, bilingual forms, and old NZ templates
Built on: Claude Vision AWS Textract Mistral OCR Vercel
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 document processing 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 cleaning companies 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 document processing 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 two or three things that decide whether this works for cleaning companies.
- Cleaning businesses run on recurring contracts and one-off quotes, both of which need fast, consistent follow-up to win.
- Quote turnaround and scheduling reliability are what most decide whether a cleaning company keeps a client past the first job.
How we build AI document processing for cleaning companies.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a cleaning company business, so value lands before the build is finished. AI for paperwork.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing cleaning companies 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 cleaning companies
The shape of the result for cleaning companies: Cuts data-entry labour by 80% on invoices, POs, and timesheets. Built on Claude Vision, 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.
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".
Is AI document processing worth it for a smaller cleaning company?
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.
Has this actually shipped for a real cleaning company?
Yes. Cuts data-entry labour by 80% on invoices, POs, and timesheets. We'll share comparable engagements on the call.
What tools do you build AI document processing on?
For AI document processing we usually reach for Claude Vision, AWS Textract, Mistral OCR, Vercel. We're tool-agnostic at heart - we pick what your cleaning company team can actually run after we hand the build over, not what looks good on a vendor sticker.
Twenty minutes, your call.
You describe what's broken. We'll tell you what we'd actually do about it.
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.