Engineering content to be cited by AI Overviews, AI Mode and LLM assistants

AI Overviews change what a ranking is worth. On a growing set of queries the answer is
assembled and shown in place, and the traffic that used to follow position 3 does not arrive.
Being cited as a source in that answer becomes the position worth holding.

So the target changed: not only rank, but be the thing the model quotes.

The approach was entity-first. Rather than writing to a keyword and adding structure
afterwards, content was built around the entities, relationships and definitions a model needs
in order to lift a passage cleanly and attribute it.

In practice:

• Semantic structure that makes claims extractable — a definitional answer in one place,
  stated plainly, not spread across three paragraphs of preamble.
• Structured data implemented across all six properties, so the machine-readable layer
  agrees with the prose.
• Testing across the portfolio, so results came from a repeatable structure rather than
  from one article happening to do well.

Portfolio AI Overview citations went from 2 to 39 in four months.

The change that matters more than the count: new content now gets cited on publication
rather than accruing citations months later. Both pages shown above were cited within days of
going live. That shifts AI visibility from something you wait for into something you can plan
around — which is what makes it schedulable work rather than luck.

Citations span Google AI Overviews and AI Mode, across commercial, integration and
definitional queries.

Google AI Overviewclick to enlarge

Google AI Modeclick to enlarge

Google AI Overviewclick to enlarge