When a law firm asks us about AI personalisation, the first question we put back is the same one every time: which part of your week, if it disappeared, would change how you feel on a Friday? We start the build from that answer.
Law firms · Applied AI
The version of AI personalisation that law firms still use a year after launch.
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
Field notes
What years of building for law firms taught us.
- Law firms bill for judgement, but a huge share of the week still goes on intake, document review, and status updates.
- New matter intake and routine client updates are the two places firms lose the most billable time to admin.
How we build AI personalisation for law firms.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a law firm business, so value lands before the build is finished. AI personalised CX.
Talk to usThe outcome for law firms
We'd call the engagement a success when law firms are using the system without thinking about us. Conversion lift of 18-32% over generic site experiences.
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.
Questions
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 law firm – 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.
Skip the pitch.
Tell us the workflow and we'll come back with what we'd build first.
Get in touch
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.