Our approach
Content Written for Volume Serves Neither Audience Well
The default content playbook targets keyword volume: find high-volume terms, produce articles, publish on a cadence. That approach was always a blunt instrument, and it maps particularly badly onto AI search, where the unit isn't a keyword but a question someone asks in full sentences.
Conversational questions are longer, more specific, and frequently have no meaningful search volume attached — which means keyword tools simply don't surface them. Meanwhile the questions with volume are often already saturated with content nobody can extract a clean answer from.
We plan from benchmarked prompt data instead: the questions your category genuinely gets asked, which you already answer, which you answer badly, and which a competitor currently owns. Then everything gets structured answer-first, so it works for a reader scanning and a model extracting.
Volume-led content
Prompt-led content
Planned from keyword search volume
Planned from benchmarked conversational prompt data
Success measured in published article count
Success measured in questions won and passages extracted
Long-form padding to hit word count targets
Length determined by what the question actually requires
Answer buried after context and setup
Answer stated first, context supporting it afterwards
Topics chosen for volume regardless of fit
Topics chosen for commercial relevance and winnability
Published and never revisited
Re-tested for extractability after each model update