One loop: make, test, target, learn.
The pieces below are not separate products you assemble. They are one optimization loop, and every part of it feeds the next.
Variants are a supply problem. We removed the queue.
Testing needs volume. Most teams can only ship a handful of creatives a month, so the test is underpowered before it starts. Our system generates and adapts variants from your existing assets across mobile and CTV specs.
- Format and aspect-ratio adaptation for every placement
- Message, offer and hook variations, not just recolours
- Brand and compliance rules applied before anything runs
Tests that end themselves.
Variants go live, accumulate signal, and get cut or scaled without waiting for a human to read a dashboard. The result is a shorter distance between "we should try this" and "we know".
- Continuous rotation instead of fixed test windows
- Statistical guardrails so early noise does not kill a good variant
- Every decision logged, so you can see why a creative was dropped
Fail hypotheses fast, then concentrate.
Instead of a fixed targeting brief, the system runs a portfolio of audience hypotheses, kills the ones that do not convert, and reallocates toward the ones that do. Fast, cheap failure is the point.
- Automatic segment proposal and expansion
- Lookalike modelling built on your real conversion events
- Exclusion logic so you stop paying to reach existing users
Reporting that survives a QBR.
Numbers you can bring into a room and defend. Attribution traced through to creative, placement and segment, with cross-screen duplication made visible rather than hidden.
- Outcome attribution down to individual creative variants
- Frequency and overlap reporting across mobile and CTV
- Anomaly and quality flags surfaced, not buried
How the model earns the right to optimize.
Optimization toward an outcome requires observed instances of that outcome. Until enough have accumulated, the system is buying information — and information costs money. This is true of every outcome-optimized platform; the difference is whether anyone tells you.
The length of the window depends on how deep in the funnel your objective sits. An install trains quickly. A funded trading account does not.
Installs, web visits, registrations. Typically converge in 7–10 days.
Add-to-cart, D1 retention, lead qualification. Roughly 10–14 days.
First purchase, KYC completion, first deposit. Commonly 2–3 weeks or more.
During the window we report on leading indicators — event volume, segment survival, creative win rate — so you can see the model converging before the efficiency numbers catch up.
The loop only closes if the data comes back.
Optimization quality is capped by signal quality. We work with the attribution stack you already run rather than asking you to trust ours.
MMP integration
Post-install and in-app event feeds from AppsFlyer, Adjust and Singular train the model on what actually happened after the click.
Server-side events
S2S postbacks and pixel paths for web and app, so deep-funnel outcomes reach the optimizer without depending on device signals alone.
Traffic quality controls
Fraud and anomaly monitoring, incentivized-traffic screening, and placement-level exclusion running inside the loop rather than as an after-the-fact report.
See the loop on your own objective.
We will walk through how the system would set up, what the learning window looks like for your funnel depth, and what you should expect to see in weeks one through six.