EvidTrace is a longitudinal public-claims intelligence platform focused on claims, evidence, contradictions, claimant reliability and narrative evolution across news media and online information environments.
Modern information systems do not only struggle with misinformation. They also struggle with memory.
Claims are made, amplified, reframed, contradicted and forgotten at enormous speed. Institutions, media outlets and public figures can change narratives over time while audiences lose the ability to track continuity.
EvidTrace approaches this as an institutional-memory problem rather than simply a fact-checking problem.
The original EvidTrace methodology emerged through analysis of frontier AI-company claims. AI systems proved a uniquely useful proving ground because narratives evolved rapidly, terminology shifted continuously and institutional incentives were unusually strong.
This environment forced the development of longitudinal evidence tracking, contradiction memory, confidence evolution and claimant continuity systems.
Those principles now extend into broader public-information analysis.
Claims should persist over time rather than disappearing into isolated news cycles.
Contradictions should remain historically visible and queryable.
Institutions and claimants should accumulate reliability trajectories across time.
Claims should be analysed within relationship structures and narrative propagation systems.
EvidTrace organises claims into longitudinal intelligence domains including AI, politics, economics, science, health and climate.
These are not simple categories. They are intended to become structured institutional-memory environments capable of tracking narrative evolution across time.
EvidTrace is evolving toward a persistent public-memory infrastructure for claims, evidence and narratives.
The long-term goal is not merely to label isolated statements true or false. The goal is to help preserve society’s ability to track informational continuity across increasingly fragmented media environments.