There is a version of AI data analytics that plumbing businesses buy off a shelf and quietly stop using inside a month. Then there's the version that's wired into your real workflow, owned by a person on your team, and still in use a year later. We only build the second one.
The version of AI data analytics that plumbing businesses still use a year after launch.
Our goal is to give plumbing businesses 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 plumbing businesses 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 |
Where most plumbing businesses engagements actually deliver value.
- Plumbers lose jobs to whoever answers the phone first, and most calls come in while they're already under a sink.
- Missed calls, slow quotes, and unchased invoices are the three leaks that cost plumbing businesses the most money.
How we build AI data analytics for plumbing businesses.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a plumbing business 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 plumbing businesses 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 plumbing businesses
The shape of the result for plumbing businesses: Owners check the business in 2 minutes instead of 2 hours. Built on DuckDB, 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 long does AI data analytics take to ship for plumbing businesses?
We aim for a working pilot inside 4-6 weeks - narrow scope, real plumbing businesses data, measurable outcome. From there it's another 6-8 weeks of hardening before you'd consider it production. Full rollouts (multiple sites, multiple teams) typically land in 3-4 months.
How do you price AI data analytics engagements?
Fixed-scope pilots first, then either project pricing or a small monthly retainer for the ongoing work. No long lock-ins, no 18-month black-box deals. Most plumbing businesses are surprised how small the first cheque is.
Anyone else in this space using AI data analytics?
Plenty. Owners check the business in 2 minutes instead of 2 hours. 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.
Who owns the code and the model setup?
You do, on delivery. We deploy AI data analytics into your own cloud account where possible, with the model setup, prompts, evals and integration code all checked into a repo you own. DuckDB sits in your account too - we don't operate it from ours.
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.