For life science marketers: why AI engines judge your pages on structure and schema, and how to stop yours being misread.
Strivenn ran an AI search audit on its own website and the tool recommended building three pillar pages that already existed. The cause was five characters at the end of a URL, and it had made three of the company's most important pages invisible to the AI engines buyers now use to build shortlists.
This conversation is for life science marketing leaders, brand managers and commercial teams who are investing in serious content and want that content to be found by AI search.
Matt Wilkinson and Jasmine Gruia-Gray work through a real diagnostic on strivenn.com, from the misclassified pillar pages to the slug and schema gaps behind them. They cover why AI engines read structural signals rather than content quality, what citation compression means for visibility, and how to move the fix out of the SEO backlog and into a revenue conversation.
Key idea: AI engines classify pages by structural signals like URL slugs and schema, so even your best content stays invisible if the architecture reads as tactical.
What you will learn:
Chapters:
Watch the full conversation, subscribe to A Splice of Life Science Marketing, you can read the original blog here.