Extending an SEO program to AI answers — the migration map
Most teams don’t start AI-visibility from zero — they extend an existing SEO program. The concepts map closely; the mechanics change shape.
What carries over
| SEO practice | GEO equivalent |
|---|---|
| Keyword list | Prompt set — the same intents, phrased as questions |
| Rank tracking | Mention rate + citation share per prompt |
| Position | Answer rank — order within entities[] |
| SERP features | Answer blocks — sources[], shoppingCards[], places[] |
| Competitor rank diff | Citation-share diff per prompt set |
| Scheduled crawls | Scheduled monitor batches — same cron, different endpoint |
The workflow ports too: keyword × market matrix → prompt × market × engine matrix. Async batches play the role rank-checker queues played.
What changes shape
- There’s no “the SERP.” Each engine answers differently — ChatGPT, Perplexity and Gemini need separate columns, and a blended number hides the story.
- Results vary per run. One check is a sample, not a measurement. Treat runs as draws and report trends over weeks.
- The answer is the content. A ranking is a position; an answer is prose your brand may be named in, cited for, or absent from entirely — mentions and citations are different events.
What to drop
Position-blended “visibility scores” that mix engines into one number, daily-rank dashboards without variance bands, and any metric that can’t be traced back to a stored response.
The hybrid view
Run both pipelines on the same prompt set: Google Search for the classic positions, the monitor endpoints for the AI layer. The fan-out queries (searchQueries[]) are the bridge — they’re literally the Google searches inside the AI answer. Covering them is SEO; being cited by them is GEO.