Intelligence, the company behind the AI design platform DesignArena, has raised a $7.9 million seed round led by Index Ventures, with participation from Conviction (Sarah Guo and Mike Vernal), A*, Valkyrie, and others. The round was announced on Monday, August 3, 2026. TechCrunch

DesignArena is used by 5.3 million people across more than 190 countries, according to the company's website. The platform lets users create websites, games, images, videos, presentations, and apps just by typing a text description. It also lets users compare different AI models side by side to find the best one for a given task. DesignArena

What makes DesignArena different from a standard AI tool is its ranking system. Users are shown two generated outputs side by side and asked to pick which is better, across websites, images, and other visual formats. Because users must log in to get their results, the company can track how people's tastes differ across countries and change over time. TechCrunch

That preference data is the real prize. The company is building a massive, worldwide dataset of human taste judgments on AI-generated visual content, collected through a consumer product that is already making money. According to co-founder Grace Li, DesignArena is currently at $60 million in annual recurring revenue. TechCrunch

Li said the company started a few weeks before graduation in 2025, with a handful of college friends trying to build an AI game engine. The product grew from there into a broader creative and comparison platform. TechCrunch

The $60 million revenue figure on a $7.9 million funding round is an unusual ratio. Startups raising that amount of money usually have far less revenue, which suggests DesignArena does not need the cash to keep operating. The round looks more like a way to bring on investors with expertise in building AI models and reaching new markets.

The investor list supports that read. Index Ventures leading fits their pattern of backing AI platform companies. Conviction's Sarah Guo and Mike Vernal bring experience from enterprise and consumer technology, respectively. A* and Valkyrie round out a group focused on early-stage AI investments.

Based on the available facts, Intelligence is positioning itself to build a system that teaches AI models what humans actually prefer. The ranking data from DesignArena could be used to train AI models to produce results people find more appealing, or to compare competing AI models on aesthetic quality in a way that goes beyond simple automated scores. The login requirement and global user base across 190-plus countries give the dataset a level of geographic and time-based detail that lab-created test sets usually lack.

The open question is whether taste, as measured by people ranking pairs of images on a consumer platform, translates into a genuinely useful training signal for AI models. Preference data has been important for training text-based AI, but teaching AI about visual taste across images, videos, websites, and apps is less proven. DesignArena's dataset covers all of these formats, which means the preference signal spans multiple types of content. Whether human taste in one format carries over to improvements in another is an open empirical question, and one that Intelligence's team will need to answer to justify the platform's strategic value beyond its consumer revenue.

The company is at an early stage of building that model layer. The consumer product exists, the revenue exists, and the data collection is running at scale. The seed round gives Intelligence the capital to start turning accumulated preference data into the kind of AI model work that investors are presumably backing.

For a platform that started as a college project in 2025 to be at $60 million in revenue and 5.3 million users roughly a year later is a pace of growth that invites scrutiny as much as celebration. The next phase will test whether the preference data DesignArena collects can become a durable advantage in the generative AI field, or whether it remains a useful but easily copied signal in a market where many companies are racing to build their own human-preference datasets.