What we're building, quarter by quarter.
The AI landscape moves fast. Here's how our methodology adapts to emerging signals.
Evaluation of external signals
First mapping and evaluation of external EEAT signals: backlinks, citations, brand mentions and domain authority.
Analyses with beta client cases
Launched the first full analyses on a panel of private-beta client cases, to validate the methodology in real conditions.
Refinement of the factors
Calibration and refinement of the 72 factors based on field feedback and data collected in beta.
Past the 500-client mark
Crossed the threshold of 500 clients analyzed, validating traction and the robustness of the methodology at scale.
EEAT periodic table v1
First public version of the 72-signal framework. Open, documented methodology.
AI Overviews tracking module
Automatic detection of citations in Google AI Overviews and ChatGPT/Perplexity answers.
Open-source skills for Claude
Publishing open-source skills for Claude, to embed EEAT analysis directly into AI workflows.
Weekly monitoring dashboard
Building a dashboard for weekly tracking of tasks and suggestions, with AI support.
Dashboard rollout to the first 1000 clients
Making the monitoring dashboard available to our first 1000 clients, with continuous tracking of the 72 signals and Core Update alerts.
Knowledge Graph automation
Automated pipeline for entity building (Wikidata, Schema, sameAs) with human validation.
NeuroEEAT API
Programmatic access to EEAT scoring for integration into CMSs and editorial workflows.