Most auto repair shops we talk to aren't short of dashboards or tools - they're short of an hour back in the week. That's the lens we put on AI demand forecasting: not a tech showcase, but a careful look at the one or two workflows where an auto repair shop is paying for the same problem to be solved twice.
Auto repair shops-grade AI demand forecasting, shipped by people who've done it before.
Our goal is to give auto repair shops a three-day weekend, so people can spend more time with their families and the people they love :)
What AI demand forecasting actually does
Forecasts that account for school holidays, NZ weather, tourist seasons, and your own promo calendar. Order the right stock, roster the right hours, plan the next quarter with actual numbers.
- 01 Combines your sales history with weather, calendar, and event data
- 02 Per-SKU and per-store forecasts, not whole-business averages
- 03 Re-forecasts weekly as new data comes in
- 04 Explains the why behind every number
Built on: Prophet DuckDB Claude BigQuery 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 demand forecasting 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 auto repair shops 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 demand forecasting 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 auto repair shops.
- Mechanics quote from underneath a car and answer the phone from underneath a car, and one of those always loses.
- Booking, quoting, and parts follow-up are the admin layer that decides how many cars a workshop can turn over in a week.
How we build AI demand forecasting for auto repair shops.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for an auto repair shop business, so value lands before the build is finished. AI sales + stock forecasting.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing auto repair shops 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 auto repair shops
What changes for auto repair shops after this lands: the work that used to need a person stays done, the work that needs a person gets done with their attention undivided. Stockouts down 35%, overstock down 22% in the first season.
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 auto repair shops 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.
Has this actually shipped for a real auto repair shop?
Yes. Stockouts down 35%, overstock down 22% in the first season. We'll share comparable engagements on the call.
What tools do you build AI demand forecasting on?
For AI demand forecasting we usually reach for Prophet, DuckDB, Claude, BigQuery, Vercel. We're tool-agnostic at heart - we pick what your auto repair shop 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.