The next learning signal is billions of humans responding, in organic settings.
FrontierPersuade connects model outputs with real-world human responses, turning behavior into quantitative feedback that teams can use in evaluation and reinforcement learning.
The problem: quality has no single right answer.
A model can produce fluent writing or a polished image without knowing whether anyone will keep reading, click or take action. In subjective areas, an output can look good in isolation and still fall flat with the people it was meant for.
What makes a story engaging, a headline compelling or an image appealing depends on the audience and context. Human ratings and model-based evaluations offer useful feedback, but they do not fully capture how people respond in real-world settings. Teams need measurable signals from those interactions to evaluate outputs and improve their models.
Endpoints built for learning.
Teams can test headlines, articles and image creatives through Quander’s API. Available signals include clicks, read and watch time, engagement and conversions. Experiment scope and instrumentation determine which signals are appropriate.
A signal is not the whole objective.
A click is evidence of a response, not a universal score for quality. Audience, exposure and context matter. Behavioral rewards should complement controlled comparisons and other quality evaluations.
The infrastructure already exists.
Quander is a Stanford-led, Accel-backed team with existing infrastructure. We’re opening it to foundation-model teams working on capabilities that are difficult to measure without interaction.
Build with Quander