Our mission
Content should earn trust — from readers and from AI
ContentMas was built by a team tired of watching great brands lose rankings to stale facts and outdated statistics. Now the same decay costs them AI citations too. ContentMas fixes both — at scale.
Why we built ContentMas
In 2023, our founding team was running content operations for a fast-growing SaaS company. We had hundreds of blog posts, dozens of landing pages, and a content team working hard to publish consistently. But our organic traffic was quietly declining — and we couldn't figure out why.
After weeks of investigation, the answer was embarrassing: our most-linked articles were full of outdated statistics. A study we'd cited from 2019 had been superseded. A regulation we'd referenced had changed. A company we'd linked to had been acquired. Google's quality signals had noticed before we did — and AI answer engines were skipping our pages entirely.
We built a spreadsheet to track it, then a script, then a proper tool. ContentMas is that tool — rebuilt from scratch to score every page across five signals, from freshness and E-E-A-T quality to GEO readiness for AI answer engines, so any team can see exactly why a page is losing visibility and how to win it back.
What we believe
Our values
Accuracy over volume
One accurate, well-maintained page outperforms ten stale ones. ContentMas optimises for content quality and trust, not content quantity.
Automation that respects editors
AI should surface problems and suggest fixes, not replace editorial judgment. Every ContentMas recommendation is a starting point, not a final answer.
Built for the AI-search era
Search is changing. AI answer engines cite sources differently than search engines rank them. ContentMas helps your content perform in both worlds at once.
Outcomes, not outputs
ContentMas measures success by ranking recovery and AI citations won — not by the number of issues flagged or reports generated.
The people behind it
Meet the team
Former VP of Content at two B2B SaaS companies. Spent a decade watching content decay quietly erode companies' organic moats.
Previously built crawling infrastructure at a major search engine. Obsessed with making web-scale content analysis accessible to small teams.
PhD in computational linguistics. Leads the ContentMas scoring engine and GEO research. Believes language models should make editors faster, not redundant.
Ten years in content marketing before moving to product. Ensures ContentMas solves real problems for real content teams.
Ready to stop content decay?
Join the content and SEO teams using ContentMas to keep their sites accurate, fresh, and citable — in search and in AI answers.