Logos is building the verification layer for the internet through a browser extension and API that fact-checks digital content in real time. The tool cross-references claims against verified source databases, triangulates coverage across 50+ outlets, and detects manipulation signals like emotional amplification and AI-generated content. It assigns a Trust Score to help users and platforms assess content credibility.
Funding
Funding not disclosed
Founders
Product
Problem
Digital content is increasingly difficult to trust, as misinformation, manipulated media, and coordinated framing spread rapidly across platforms. Readers and platforms lack accessible tools to systematically verify claims, detect omissions, and assess the credibility of what they encounter online.
Solution
Logos provides a browser extension and API that functions as a real-time fact-checking and credibility scoring layer for digital content. The tool cross-references factual claims against verified source databases, triangulates coverage patterns across 50+ outlets per story, and identifies what articles deliberately omit. It also detects manipulation signals such as emotional amplification, false balance, and coordinated framing, while flagging synthetic content and deepfakes. Each piece of content receives a Trust Score, giving users an immediate, quantifiable measure of reliability. The infrastructure is designed to scale beyond individual users, positioning itself as a credibility layer that platforms and AI systems can integrate into their information retrieval pipelines.
Target Audience
Primary users are individual readers seeking to verify online content, as well as platforms, publishers, and AI systems that need an automated credibility scoring layer for information retrieval and content moderation.
Features
- Claim verification engine that cross-references factual statements against verified source databases
- Source triangulation that compares coverage patterns across 50+ outlets per story to identify consensus or divergence
- Omission detection that identifies what articles deliberately leave out based on study sample sizes and contradicting data
- Manipulation signal detection for emotional amplification, false balance, and coordinated framing
- Emotional analysis that tracks fear, anger, and outrage signals in content
- AI detection that flags synthetic content and deepfakes
- Trust Score output that provides a single numerical credibility rating for any piece of content