A bias and accuracy score for everything you read

You open your feed in the morning. A quote from a politician, a chart with no source, a friend sharing a headline that looks a little too neat. You believe some of it, ignore the rest, and you are never quite sure which call was right. The problem is not one bad article. It is a steady background doubt about everything you read, and the feeling that the loudest voices are the ones deciding what you think.

This is a small tool you add to your web browser. As you scroll through your feeds, it quietly checks the claims in front of you. It marks the ones that can be checked, looks them up against the original source, and shows a simple rating for how true a claim is and whether it leans left or right politically. Hover over anything and a small card shows the evidence it used, so you can judge for yourself.

Over time it does more than rate single claims. It shows you the shape of your own reading, the topics where you only ever hear one side, and what the other side of a story actually says. And it gives news outlets a stamp they can display to show their reporting was checked.

What makes it different is simple. The check happens right where you are reading, on the claim itself, the moment you see it, with the evidence one hover away. You do not have to leave your feed, copy a link into another website, or take a rating on faith. The proof sits next to the sentence.

Three forces converged at once. Worry about misinformation is now mainstream: 58 percent of people worldwide say they are worried about what is real and fake online, rising to 73 percent in the United States (Reuters Institute, 2025). The institutions that used to label claims are pulling back, with Meta ending its United States third party fact checking program in 2025 and handing the job to crowd sourced Community Notes (Al Jazeera, 2025). And the threat now sits at the top of the global agenda, with the World Economic Forum ranking misinformation and disinformation the most severe short term risk facing the world (World Economic Forum, 2024).

At the same time, the tools to check a claim on the fly got cheap. Language models can now read a claim and cross reference a source inside a browser, where the same work used to take a newsroom. So the audience that wants a second opinion got large and vocal exactly as the cost of giving one fell. The opening is real. The question is whether a new entrant can actually hold it.

Taken together, the signals show measurable, mainstream concern about misinformation and a market that already pays for help judging what it reads. A funded bias comparison app, an enterprise trust rating business, and a crowd of existing browser tools all point to real, proven demand.

This is a real problem with a genuine why now and a proven willingness to pay, and it still does not clear the bar for a breakout. The demand is visible but already chased by Ground News, NewsGuard, Community Notes, and a crowd of AI extensions, so a small team has no structural edge to defend. The two hardest parts, building a per claim checker accurate enough to trust and being believed by readers who distrust every rater, are exactly the parts nobody has solved. The biggest risk is trust: an AI that is confidently wrong, or a rating accused of bias, kills the product on contact. For this to work, the checker would need to be accurate and transparent enough that even a skeptic believes it, wrapped in a wedge that Ground News and the platforms cannot simply copy.