Built by the people who burned the budgets.
Patternix is developed — and actively built — by a team of data-marketing specialists and ML/AI engineers. Between us we have produced creatives, launched direct-to-consumer brands, and run more than 55 crowdfunding campaigns. We built the tool we always wished we'd had.
Two disciplines, one team.
Creative intelligence only works when the people who understand marketing and the people who understand models build it together. That's the team behind Patternix.
Data marketing operators
We've spent a decade inside performance accounts — buying media, reading delivery, and living with the cost of every creative that didn't land. Patternix encodes what we learned the expensive way.
ML & AI engineers
The other half of the team builds the models — multimodal creative fingerprinting, lifecycle classification, and the Andromeda decoder that reads how Meta's algorithm responds.
Builders who ship products
We didn't come from a lab. We produced the creatives, launched the brands, and ran the campaigns ourselves — so the tool is built for the people doing the work, not for a slide deck.
55+ launches. Real money. Real markets.
Our team has launched and scaled over 55 crowdfunding campaigns on Kickstarter and beyond — taking physical products from concept to funded, and from funded to direct-to-consumer brands with live paid acquisition.
Every one of those launches lived or died on creative. That is the experience baked into Patternix: we know what a winning creative looks like before the data confirms it, because we've shipped hundreds of them.
Cross-market experience means Patternix is built for creative that travels — across languages, audiences, and platforms.
We were tired of guessing.
We'd sit through learning phases watching budget burn, arguing about which creative to scale on a feeling, and starting the next batch with no idea what actually worked. The platforms kept the “why” in a black box. So we built the system that opens it — decoding every creative into structured signals, reading how the algorithm responds, and calling the outcome early enough to act.