Generative Engine Optimization
Be the source a model reaches for, in the moment it answers.
What generative engine optimization means
Generative engine optimization prepares content for retrieval-augmented systems. It focuses on passage-level clarity, consistent entity naming, citable statistics with sources, and machine-readable summaries so a model can quote you accurately rather than paraphrase a competitor.
You probably came here because of one of these.
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Models paraphrase your material inaccurately, or attribute it elsewhere
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Your statistics travel without your name attached
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Long pages are retrieved for the wrong passage entirely
What you actually receive
How it runs, and when things land
- Weeks 1–2
Audit
Passage-level audit and a statistic inventory showing what is sourced and what is not.
- Weeks 3–6
Build
Content refactored into self-contained passages; llms.txt and summaries generated.
- Weeks 7–12
Ship
Retrieval testing across models, with gaps fed back into the content plan.
- Month 4+
Compound
Quarterly statistic re-verification so nothing published goes stale unnoticed.
Who this is wrong for
Teams unwilling to source their claims. The whole method depends on statistics that carry a named source and a date, and unsourced content will not be quoted preferentially.
If that describes you, say so on the first call. We would rather lose the work than take it on and underdeliver.
Often runs alongside
Frequently asked questions
GEO prepares content for retrieval-augmented systems. It focuses on passage-level clarity, consistent entity naming, statistics that carry their source, and machine-readable summaries so a model can quote you accurately rather than paraphrase a competitor.
Find out what AI says about you right now.
We run your real buyer prompts through four assistants and send you the transcript, with your position and your competitors’. No charge, no call required to receive it.
Typical turnaround: 3 working days.