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Generative engine optimization · GEO

Generative engine optimization that makes you easier to find, trust and recommend.

Our generative engine optimization and AI search optimization services start with measurement. We show where your brand disappears, which competitors and sources replace it, and which content, entity or technical fixes deserve attention first.

  • No invented visibility score
  • No sales call
  • Raw evidence included
A central company entity connected to independent evidence and four synthesis systems that produce a verified answer.
Original editorial illustrationGEO strengthens the evidence paths that make an entity easier to retrieve, corroborate and recommend.

The operating principle

GEO is not keyword placement for chatbots.

Generated answers are assembled from retrievable evidence. A page can be well written and still lose if the engine cannot access it, identify the company, verify the claim or connect it to the buyer’s question. That is why our GEO work begins with a reproducible answer baseline rather than a content calendar.

What the service covers

Six connected jobs, measured as one system.

Every recommendation must trace back to an observed answer, source pattern or technical condition.

01

Measure the answer layer

We run 50–150 buying questions across ChatGPT, Claude, Gemini and Perplexity. Each prompt runs three times so variance is visible.

02

Find the evidence gap

We compare mentions, citations, recommendations and source patterns against named competitors at the prompt level.

03

Repair technical access

We inspect crawler controls, canonical signals, schema, entity facts, discovery files and the structure engines need to extract a clean answer.

04

Build answer-worthy pages

We create or improve pages around one clear intent, with direct answers, useful detail, original evidence and explicit entity relationships.

05

Strengthen corroboration

We identify where independent sources, review surfaces, directories, expert references or first-party datasets would make a claim easier to trust.

06

Remeasure the same questions

The original prompt set becomes the control. Changes are judged against the dated baseline, not a generic visibility score.

Who this is for

Use GEO when the answer can influence a valuable decision.

Good fit

  • Buyers compare several named vendors before contact.
  • Your category has enough independent evidence to inspect.
  • A team can implement content and technical changes.
  • You want the evidence, even when the baseline is uncomfortable.

Wrong fit

  • You want guaranteed placement in a specific answer.
  • You need fabricated reviews, citations or third-party consensus.
  • You expect one page rewrite to replace product-market evidence.
  • You cannot publish, correct entity facts or improve the site.

Start with proof

See the output before you buy the service.

The sample dashboard exposes the prompt grain, scoring, citations, flagged claims and action sequence. The methodology publishes the limits. Use both to judge whether the work is rigorous enough for your team.

Interactive sampleOpen the evidence dashboardPublished methodInspect the scoring rubric

Common questions

What should you know before starting?

01How does generative engine optimization work?+

GEO improves the evidence an answer engine can discover, understand and corroborate. The work normally spans technical access, answer-shaped content, entity consistency, primary evidence and trustworthy third-party references.

02Is GEO replacing SEO?+

No. Search visibility still supports discovery and authority. GEO adds a second outcome: whether a brand is mentioned, cited or recommended inside a generated answer. Strong technical SEO remains part of the foundation.

03What does a GEO service actually deliver?+

CitedMetrics begins with a measured baseline across real buying questions. The output includes prompt-level evidence, competitor share of voice, source analysis, technical findings and a prioritised implementation plan.

04How long does GEO take to work?+

Technical fixes can be discovered quickly, but answer changes depend on recrawling, source freshness and model retrieval. We set a dated baseline, implement in priority order and remeasure instead of promising an arbitrary ranking date.