What We Build - Growth Engineering Services
Growth teams rarely run out of ideas. They run out of throughput — the ability to build a variant, measure it properly, and decide, all before the idea goes stale.
We build the infrastructure that removes that constraint: landing page systems marketing can operate directly, experiment frameworks with reliable exposure logging, event schemas designed around real questions, and the join between acquisition and revenue that makes a result meaningful.
5 min read5 sectionsWhat We Build
What you'll take away
- Experiment throughput is the growth mechanism. Everything we build is aimed at increasing it.
- Instrumentation comes first. An experiment you cannot measure precisely is an opinion with extra steps.
- Decouple growth from the product release cycle, or growth inherits its speed.
- Fixed scope, your infrastructure, documented handover.
What we build
- Landing page and content systems
- Marketing creates and edits pages directly, while performance, SEO structure and tracking are guaranteed by the system rather than by discipline. Usually the single largest increase in test throughput.
- Experiment infrastructure
- Assignment, exposure logging, guardrail metrics and standardised result views. Whether we build or integrate a platform, the priority is exposure logging you can trust — most invalid results come from broken assignment.
- Event schema design
- Events designed around the questions you need answered rather than around what is easy to instrument. This is the difference between diagnosing a drop-off and speculating about it.
- Acquisition-to-revenue join
- Source, product behaviour and revenue connected in a warehouse, so a conversion improvement can be traced to a business outcome instead of a local metric.
- Onboarding and activation instrumentation
- Every step of the activation path measured, with intervention triggers when users stall. Activation is where the largest conversion gains usually are.
- Checkout and funnel optimisation
- Diagnosis and rebuild of the highest-leverage steps, with automated monitoring so a regression on one browser surfaces in minutes rather than days.
- Lifecycle messaging infrastructure
- Behaviour-triggered messaging driven by real product events rather than time-based schedules. Once the event schema exists, this is comparatively cheap and consistently outperforms.
How we work
Measure current throughput
How many meaningful tests ran last quarter, and where did the time go — building, instrumenting, waiting or deciding? The bottleneck is rarely where the team assumes it is.
Design the event schema
Start from the questions — where do signups drop off, which activation step predicts retention — and design events that answer them. Everything downstream depends on this being right.
Build the revenue join
Connect acquisition source, product behaviour and revenue in the warehouse. Until this exists, every result is a local optimisation with no visible business connection.
Remove the biggest throughput constraint
Usually page creation. A self-serve landing page system with guaranteed performance and tracking typically doubles the number of tests a team can run.
Add experiment tooling
Assignment, exposure logging, guardrails and standardised result views, so analysis stops being bespoke and decisions stop being delayed.
Hand over with the operating rhythm
Documentation, runbooks and a working session covering how to run the cycle — hypothesis, build, measure, decide — without us.
What you get
- A self-serve page system
- Marketing ships pages without a development cycle, with performance budgets and tracking enforced by the system.
- Working experiment infrastructure
- Reliable assignment and exposure logging, guardrail metrics and result views your team can read without an analyst.
- A documented event schema
- Designed around your questions, with naming conventions and a plan for extending it as the product changes.
- Warehouse models joining acquisition to revenue
- So conversion results connect to contribution margin rather than stopping at a click-through rate.
- A measured throughput improvement
- Tests per quarter before and after, which is the metric this whole discipline exists to move.
Typical projects
| Constraint | What we build | Effect |
|---|---|---|
| Every page change needs a developer | Self-serve landing page system with guaranteed tracking | Test throughput typically doubles |
| Results are argued about, not read | Reliable exposure logging and standardised result views | Decisions in hours instead of weeks |
| Nobody knows where signups drop off | Onboarding instrumentation end to end | The activation bottleneck becomes visible |
| Conversion wins do not show in revenue | Acquisition-to-revenue join in the warehouse | Experiments measured on contribution margin |
| Lifecycle email is time-based and ignored | Behaviour-triggered messaging on product events | Meaningfully higher engagement |
| A checkout regression ran for days | Automated funnel monitoring by step, device and browser | Regressions caught in minutes |
Why this pays back
- Throughput compounds
- A team running twelve tests a quarter learns roughly six times faster than one running two. The difference is almost entirely infrastructure, and it compounds every quarter afterwards.
- Decisions become defensible
- Reliable exposure logging and guardrail metrics mean results are trusted rather than debated. Most of the time lost around experimentation is spent arguing about validity.
- Marketing stops waiting
- A self-serve page system removes the largest queue in the loop. That change alone usually justifies the project.
- Results connect to revenue
- A conversion improvement that cannot be traced to contribution margin is hard to fund twice. The warehouse join fixes that permanently.
Frequently asked questions
What does a growth engineering engagement include?
- Typically an event schema design, the acquisition-to-revenue join in a warehouse, a self-serve landing page system, experiment infrastructure, and instrumentation of onboarding or checkout. Scope is set after measuring where your current throughput constraint actually is.
Should we build or buy an experimentation platform?
- Usually buy. The differentiating work is the event schema, the revenue join and the self-serve page infrastructure — not the assignment logic. Whichever route you take, verify exposure logging is reliable, because that is the most common source of invalid results.
How is this different from a CRO agency?
- A CRO agency runs tests. We build the infrastructure that lets your team run more of them, faster, with results you can trust. The two are complementary — but if your constraint is throughput rather than ideas, infrastructure is the higher-return investment.
Do we need a data warehouse first?
- For page systems and basic experiments, no. For connecting conversion results to revenue and for cohort analysis, yes. If you do not have one, building it is usually the first phase, and it is what makes everything afterwards measurable.
Who maintains this afterwards?
- Your team, with documentation and a handover session covering the operating rhythm. The systems are built to be operated by marketers and analysts, not only by engineers — that is the point of a self-serve page system.
How many tests did you actually ship last quarter?
We build the page systems, experiment infrastructure and instrumentation that raise that number — and the warehouse join that connects the results to revenue.