We've worked with enough operators in New York to know that the brief that arrives in our inbox is rarely the brief that ends up shipped. The first thing we do on any AI personalisation project is sit with your team for a day before we propose anything.
Built and supported here - the way a New York business would actually use it.
Our goal is to give New York businesses a three-day weekend, so people can spend more time with their families and the people they love :)
What AI personalisation actually does
Every customer sees the right product, message, and offer - based on what they've bought, browsed, and asked. Built on first-party data, no creepy tracking required.
- 01 Per-customer recommendations across web and email
- 02 Dynamic landing pages tailored to traffic source
- 03 Lifecycle messaging triggered by real behaviour
- 04 GDPR + NZ Privacy Act compliant by default
Built on: Claude Vercel Edge Postgres Klaviyo
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 personalisation 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 New York 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 personalisation 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 |
What's different about doing this work in New York.
- New York runs on finance, law, media and a professional services machine that bills by the hour - AI here earns its place by giving that time back, not by being impressive in a demo.
- Wall Street firms, Big Law, and a media and advertising industry that never fully stops, all sitting on top of the highest labour costs in the country. New York teams want AI that survives contact with a real client deadline.
We work with teams across New York: Manhattan · Brooklyn · Queens · Midtown · Financial District · Long Island City.
Talk to us about this →
How we build AI personalisation for a New York team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your New York business, so value lands before the build is finished. AI personalised CX.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing New York 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 New York teams
Conversion lift of 18-32% over generic site experiences. For New York teams, 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
When does AI personalisation actually pay back?
Inside the first quarter, in our experience. We pick the first slice specifically because it's the highest-leverage workflow for a New York - so the savings start landing before the rest of the build is finished.
Do you do hourly billing or fixed price?
Fixed price for the pilot, every time. After that it's your call - fixed price per milestone or a small monthly retainer for ongoing iteration. We don't run open-ended T&M because it disincentivises us from finishing.
Anyone else in this space using AI personalisation?
Plenty. Conversion lift of 18-32% over generic site experiences. 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.
Will this run on our own infrastructure?
Yes, where it makes sense. AI personalisation can sit entirely in your cloud account, with model calls routed through endpoints you control. We default to Claude, Vercel Edge, Postgres, Klaviyo but the architecture supports your existing platform choices.
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.