Your campaigns, run by a system that learns.
StarrisedAds hands creative production, audience discovery, A/B testing and optimization to AI — across mobile and Connected TV. You set the objective. The system finds the way there, and shows its work.
Five jobs that used to need five people.
Most platforms automate bidding and call it AI. We automate the whole loop — from making the creative to deciding who sees it to explaining what happened.
Creative production at volume
Generate and adapt variants across mobile and CTV formats from your existing assets. Enough versions to actually test, without a production queue.
A/B testing on autopilot
Variants launch, underperformers get cut, winners get budget. The loop runs continuously instead of waiting on a weekly review call.
Audience discovery
The system proposes and tests audience hypotheses, fails them fast, and concentrates spend on the segments that convert — not the ones that look good on paper.
Campaign intelligence
Reporting that answers why, not just what. Which creative, which placement, which segment moved the number — traced end to end.
Brand and performance in one loop
CTV builds attention, mobile captures the action, and both are planned, capped and measured as a single campaign rather than two budgets.
Measurement that closes the loop
Post-install and post-signup events flow back through your MMP — AppsFlyer, Adjust, Singular — so the model trains on outcomes, not proxies.
Week one will not be your best week.
Any system that optimizes toward an outcome has to observe that outcome first. During the learning phase, delivery is deliberately exploratory: costs swing, segments get tested and discarded, and the numbers look worse than they will. That is the model paying for information.
We would rather tell you this now than explain it in week two. Here is what the window actually looks like.
Wide creative and audience sampling. Watch reach and event volume, not cost efficiency.
Weak variants and segments drop out. Cost per outcome starts trending and volatility falls.
The model is trained. Now efficiency targets are fair to judge, and budget can move up.
Different objectives need different signals.
A shopping campaign and a trading app campaign do not fail the same way. We tune the optimization loop to your category — different training events, different audience logic, different watch-outs.
Shopping & E-commerce
Value-based bidding toward first-order ROAS, with learning windows planned around your promo calendar.
02 / GAMINGGaming
Optimized past the install — toward D1/D7 retention and payer LTV, not raw volume.
03 / FINANCEFinance & Fintech
Deep-funnel training on KYC completion and funded accounts, filtering registration-only traffic.
04 / WEB3Web3 & Trading Apps
Market-by-market eligibility screening, incentivized-traffic controls, and deposit-level optimization.
Three steps, then the system takes over.
Connect your objective
Tell us the outcome that matters — installs, purchases, funded accounts — and connect your MMP or pixel so the model can see it.
Hand over creative
Give us what you have. The system produces the variants and formats it needs for mobile and CTV placements.
Read the loop, not the day
Watch the learning phase close. Once it converges, scale budget against numbers you can defend internally.
Bring us a hard objective.
Tell us the outcome you are being measured on and the market you need it in. We will tell you honestly whether it is a fit — and what the first six weeks should look like.