AI personalisation that lives in your stack, not on a vendor's roadmap. Shipped from Pennsylvania.

Our goal is to give Philadelphia businesses a three-day weekend, so people can spend more time with their families and the people they love :)

Philadelphia businesses don't need another generic AI pitch. AI personalisation only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Pennsylvania.

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

What you actually get

Every engagement is scoped and quoted up front. This is what is in the box.

How AI personalisation compares

The two things most businesses do instead, and where each one runs out.

 Hiring for itAn off-the-shelf toolKiwi Dynamics
Fit to how you workFits perfectly, costs a salaryYou bend your process to suit the toolBuilt around the workflow you already run
Time to something usefulImmediate, and permanentQuick to switch on, slow to make fitA working slice in weeks, then hardened
Who owns the dataYou doThe vendor, on the vendor's termsYou do, in your own accounts
When it breaksThat person sorts it, if they are inA support queue and a ticket numberThe people who built it
What it costsA salary, every year, foreverPer seat, forever, used or notScoped and quoted up front

What we keep seeing in Philadelphia.

  • Philadelphia runs on healthcare, education and a manufacturing base that never fully left - AI here means modernising operations built up over decades, not starting from a blank slate.
  • One of the largest hospital and university networks on the East Coast, alongside a manufacturing and logistics base with real history. Philadelphia businesses want AI that fits into how things already run, not a rip-and-replace.

We work with teams across Philadelphia: Center City · University City · Fishtown · Manayunk · Old City · South Philly.

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How we build AI personalisation for a Philadelphia team.

We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Philadelphia business, so value lands before the build is finished. AI personalised CX.

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How the work runs

The outcome for Philadelphia teams

Conversion lift of 18-32% over generic site experiences. For Philadelphia 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

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*Every engagement is scoped and quoted up front. Results vary by workflow and business.

How much is not automating costing you?

Nine hours a week of admin is 468 hours a year. With Kiwi Dynamics, that drops to about 52.

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*Based on 9 hours a week of admin at Kiwi Dynamics' typical 80% automation rate. Your number may vary, the calculator uses your own.

FAQ

How fast could we have AI personalisation in production?

Eight to ten weeks for most Philadelphia businesses. 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 does AI personalisation cost for a Philadelphia?

Pilots start from a fixed scope priced to land a measurable result inside 6 weeks. Pricing depends on data volume, integration complexity, and whether you need us on managed services afterwards. We'll quote precisely after a 30-minute scoping call.

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.

What if our Philadelphia 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 personalisation specifically, we typically run that work on Claude, Vercel Edge, Postgres, Klaviyo and assume messy starting conditions from day one.

One short call.

Tell us what you're trying to fix. We'll come back inside a working day.

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Tell us what you're trying to do and we'll reply with how we'd build it - no obligation.