Every organization is sitting on text — reviews, support chats, session transcripts, open-ended survey answers. Plenty of tools can predict from that text. Almost none can tell you why in words a person can act on. PsyProxy reads text the way psychology reads people: it scores every passage against hundreds of named, research-grounded measures — Frustration, Perceived Usefulness, Trust, Life Satisfaction — and then explains whatever outcome you care about with one short, readable equation built from a handful of them.

Here’s what that looks like in practice. We pointed PsyProxy at 300,000 Amazon video-game reviews and asked it to predict each review’s star rating from the text alone. It beat sentiment tools, dictionaries, topic models, and a ChatGPT-based scorer — but the point is how. The whole model is fifteen named measures with visible weights. Life satisfaction language pushes ratings up. Moral-wrongness language, the reviews accusing sellers of scams and false advertising, pushes them down hardest. You don’t have to trust a black box; you can read the model like a sentence and check it against your own intuition.

That readability is why PsyProxy serves two audiences at once. For science, the measures live in fixed, versioned “lenses,” so results are comparable across studies and teams. Any published equation carries its lens marker, like [PsyProxy.ai/Lens/PL40], so other researchers can rerun and challenge the equation on their own data. For business, it turns text into decisions you can defend: which feelings actually drive your ratings, churn, or complaints, and therefore what to fix, with one equation you can rerun every quarter against the same fixed measures. Across ten benchmark tasks, from drug-treatment effectiveness to suicide-risk detection, PsyProxy took the best average rank of any approach while staying entirely inspectable.