Behavioral patterns no analyst would find
AI surfaces signals in your instrumentation data — drop-off patterns, session sequences, micro-behaviors — that a human analyst working in dashboards would never see. We find the ones worth acting on.
AI Growth Engine
Turn a live product into a growing one.
Turn a live product into a growing one.
Evidence over guesswork.
Data-driven growth — instrumentation, experimentation, activation, and retention work that compounds, run by a senior team. We measure first, then move.
—— What AI changes
This isn't a traditional growth retainer. Here's what AI changes.
Experimentation at a speed traditional teams can't run
Instead of running 2–3 tests a month, AI-assisted hypothesis generation and automated variant analysis lets us run a meaningful experiment cadence every week — and read the results honestly.
Predictive churn before it happens
Traditional retention is reactive — you try to win back users who've already left. AI churn modeling tells you who's drifting before they're gone, so lifecycle messaging hits when it still matters.
Personalized onboarding per user, not per segment
Instead of two or three A/B variants of your onboarding flow, AI-driven personalization adapts the first-run experience to individual behavior in real time — more users reach their moment of value.
Intelligent lifecycle messaging that adapts
Time-based drip sequences are dead. AI-triggered lifecycle messaging sends the right thing based on what the user actually did — or didn't do — inside the product.
Attribution that goes beyond last click
AI attribution modeling surfaces the real path to conversion across channels — so you stop over-investing in the channel that gets the credit and start investing in the one that does the work.
—— What we do
Growth, instrumented.
Four disciplines that work as one system — measure what users actually do, then improve it deliberately, sprint after sprint.
Analytics & Instrumentation
Event tracking, funnels, and dashboards that reflect how people really use your product — including AI-assisted pattern recognition that surfaces what dashboards alone won't show.
Experimentation & A/B Testing
A disciplined testing practice — clear hypotheses, controlled variants, and honest reads on the result, with AI-accelerated hypothesis generation from your actual data.
Activation & Onboarding
Find where new users stall before they reach value, then rebuild the first-run experience with AI-personalized flows that adapt to individual behavior.
Retention & Lifecycle
Lifecycle messaging, re-engagement, and habit loops that bring people back — with AI-triggered messaging based on what users actually do, not just time elapsed.
—— Why growth plateaus
Why growth plateaus.
Most plateaued products share the same few patterns. Naming them is the first step — here's what we do about each one.
Flying blind, no instrumentation
No reliable tracking means no honest answers. Teams argue from anecdotes and ship changes they can't measure.
A leaky activation funnel
Sign-ups come in, but most never reach the moment of value. More traffic only makes the gap more expensive.
One-off tactics, no system
A growth hack here, a redesign there. Without a repeatable loop, results don't accumulate and nothing compounds.
Shipping constantly, moving slowly
The team is executing. Features are going out. But none of it moves the metrics. Without a measurement loop connecting effort to outcome, the team loses confidence — and so does the board.
—— Services
A service for every growth lever.
Engage us for one focused piece of the engine or the whole loop — each stands on its own, and each feeds the next.
Analytics & Instrumentation
Event tracking, funnels, and dashboards that show how people really use your product.
A/B Testing
Controlled variants and honest reads, so wins are repeatable rather than lucky.
Onboarding & Activation
Rebuild the first run so more new users reach the moment that makes the product stick.
Retention & Lifecycle
Lifecycle messaging and habit loops that bring people back — the metric that compounds.
Funnel Optimization
Find where the funnel leaks and tighten each step so more of your traffic converts.
Growth Experiments
A weekly cadence of testable hypotheses, run end to end — keep what earns its place.
Not quite ready for growth work?
Build the product first with AI-Native Development →
Automate growth workflows with AI Agentic Systems →
—— How it works
Measure, then multiply.
A loop, not a project. Each phase feeds the next, and the cadence keeps running long after the first wins land.
1
Phase 01
Measure
Instrument the product properly and establish a clean baseline — so we know where you stand before we change anything.
2
Phase 02
Hypothesize
Read the data to find the leaks, then turn them into a prioritized list of testable hypotheses worth the team's time.
3
Phase 03
Experiment
Ship tests on a weekly cadence, measure honestly, and keep the changes that earn their place. Discard the rest without ego.
4
Phase 04
Scale what works
Roll proven wins out fully, fold them into the product, and feed the result back into the loop so growth compounds.
—— The engagement, week by week
A growth engagement, plotted out.
A typical engagement, week by week. The phases overlap — experiments start running before instrumentation is fully wrapped, and the loop keeps going.
MEASURE
HYPOTHESIZE
EXPERIMENT
SCALE
WEEK 1Apr 06
WEEK 2Apr 13
WEEK 3Apr 20
WEEK 4Apr 27
WEEK 5May 04
WEEK 6May 11
WEEK 7May 18
WEEK 8May 25
WEEK 9Jun 01
WEEK 10Jun 08
WEEK 11Jun 15
WEEK 12Jun 22
—— Selected work
Products we've helped ship and scale.
A few of the products we’ve shaped — from connected fitness and healthcare to mobility and IoT.
—— Where growth leads
Growth connects to everything else we build.
The AI Growth Engine is powerful on its own — and even more powerful as part of a full engagement.
AI Audit
Know exactly where AI fits before you spend a dollar building. Strategy, data readiness, and a prioritized roadmap.
Explore the AI Audit →AI Prototype Sprint
Validate your idea with a clickable prototype in three weeks. Real answers, not guesses.
Explore the Prototype Sprint →AI-Native Development
Production-grade AI products — designed, built, and shipped by a senior team in weeks, not months.
Explore AI-Native Development →AI Agentic Systems
Autonomous AI agents that handle complex workflows, make decisions, and take action across your systems.
Explore AI Agentic Systems →Intelligent Automation
AI-powered automation for the repeatable work eating your team's day — document processing, data pipelines, compliance checks, and more.
Explore Intelligent Automation →—— Common questions
What teams ask first.
Readiness, the tooling, how we measure impact, paid versus product-led, advisory versus done-for-you, and how AI changes the game.
When is my product ready for growth work?
Once you have a live product and a steady trickle of real users. Growth work multiplies something that already exists — if you're still searching for product-market fit, the honest move is to validate that first. We'll tell you which situation you're in.
What tools do you use?
We're tool-agnostic and fit your stack rather than forcing a migration. Typically that means a product analytics layer for events and funnels, an experimentation framework for A/B tests, and a lifecycle-messaging tool for activation and retention campaigns.
How do you measure impact?
We agree on the metrics that matter before we start and set a baseline. Every experiment is judged against a control, and we report what moved, what didn't, and what we're doing about it — no vanity numbers, no cherry-picking.
Paid acquisition or product-led growth?
Usually product-led first. Until activation and retention are healthy, paid acquisition pours water into a leaky bucket. We fix the product mechanics that compound, then layer in acquisition once each new user is worth keeping.
Is this advisory or done-for-you?
Either. A senior team can embed and do the instrumentation, experiments, and shipping hands-on, or work alongside yours to set up the system and coach the practice. We scope it to how much your team wants to own.
How is AI-powered growth different from what a traditional growth agency does?
Traditional growth work relies on human analysts reading dashboards and running a few tests a month. AI changes the speed and depth — we surface behavioral patterns no analyst would find, generate hypotheses from your actual data, predict churn before it happens, and personalize onboarding per user instead of per segment. The discipline is the same; the signal-to-noise ratio is dramatically better.
—— Field notes
What we’re writing about.
Field notes from the studio — what we’re learning about AI products, agent UX, and the messy reality of shipping software in 2026.
