Accounting firms are some of the most efficient operators in New Zealand - which means the easy wins from AI data analytics are usually already taken. The brief we accept is the one focused on the workflow you've tried to fix twice and given up on.
For accounting firms who want results in weeks, not a year-long transformation programme.
Our goal is to give accounting firms 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 accounting firms 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 accounting firms we work with.
- Accounting firms carry seasonal peaks where document chasing and data entry crowd out the advisory work that actually pays.
- Client document requests, reconciliations, and deadline reminders are the repetitive load behind every busy season.
How we build AI data analytics for accounting firms.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for an accounting firm 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 accounting firms 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 accounting firms
We'd call the engagement a success when accounting firms are using the system without thinking about us. Owners check the business in 2 minutes instead of 2 hours.
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
What's the realistic timeline for AI data analytics with an accounting firm?
Most accounting firms have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational - your team gets to use the thing well before the engagement is "done".
Is AI data analytics worth it for a smaller accounting firm?
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.
Can you walk us through a comparable build?
Yes - on the first call we'll pick the closest engagement we've shipped to what you're describing and walk through the outcome, the headcount and the time it took. Owners check the business in 2 minutes instead of 2 hours.
What happens if we want to swap a vendor out later?
AI data analytics is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. DuckDB, Claude, Metabase, BigQuery, Vercel AI SDK are our defaults, but the build is intentionally portable.
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.