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 data analytics: 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.
Built for the auto repair shop who's already tried the off-the-shelf option and bounced off it.
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 data analytics actually does
Stop digging through dashboards. Ask plain-English questions of your sales, jobs, and customer data - get charts, summaries, and the why behind the numbers in seconds.
- 01 Natural-language queries over your Xero, Shopify, CRM data
- 02 Weekly auto-summaries delivered to inbox or Slack
- 03 Anomaly detection - flags weird weeks before you notice
- 04 Forecasts that explain themselves, not black boxes
Built on: DuckDB Claude Metabase BigQuery Vercel AI SDK
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 data analytics 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 data analytics 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 auto repair shops we work with.
- 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 data analytics 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. Ask your data in English.
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
Owners check the business in 2 minutes instead of 2 hours. For auto repair shops, that almost always shows up as fewer interruptions and a calmer week, not a dashboard chart.
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 fast could we have AI data analytics in production?
Eight to ten weeks for most auto repair shops. Faster if your data is in good shape and slower if we're untangling a legacy integration first. We'll give you a realistic number on the scoping call rather than the optimistic one.
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.
Do you have proof this works for auto repair shops?
Direct case study: Owners check the business in 2 minutes instead of 2 hours. Happy to walk you through full numbers on a call.
What if our auto repair shop doesn't have any data ready?
Most don't. Getting the data into shape - ingestion, cleaning, the lightweight contracts you need before any model is useful - is part of the engagement. For AI data analytics specifically, we typically run that work on DuckDB, Claude, Metabase, BigQuery, Vercel AI SDK and assume messy starting conditions from day one.
One reply, one direction.
We don't run sequences or follow-up automation. One useful answer, one decision on your side.
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.