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HomeAI EnginesClaude
Anthropic answer intelligence

Understand how Claude synthesizes your brand evidence.

Measure recommendations, source-backed claims and long-form comparison context across prompts where detailed reasoning matters.

Start free trial Run a snapshot
Claude logo
Engine characteristic Long-form synthesis
Evidence layer Primary documentation
Measurement lens Claim consistency
Claude measurement

Preserve the answer and the evidence behind it.

The formal project keeps question, answer, source and competitor context together for repeatable review.

InsightWonder / Claude Sample data

Visibility evidence

Last 30 days
Synthesis presence 36% +4%
Supported claims 21 +4%
Comparison wins 12
Narrative gaps 9
Buyer question Brand status Evidence
Compare modular sofa construction Recommended Primary page
Explain low-VOC furniture standards Mentioned Primary page
Evaluate washable upholstery claims Gap Competitor cited

Anthropic documents server-side web search as a tool available in supported Claude API workflows.

Anthropic tool use ↗
Engine-specific lens

What to inspect in Claude.

Keep a shared core framework, then add the engine-specific evidence needed to explain differences.

01
Long-form synthesisRecord whether search or grounding influenced the answer.
02
Primary documentationConnect visible claims to exact source pages and domains.
03
Claim consistencyCompare the same question across measurement windows and competitors.
Optimization loop

A repeatable Claude visibility workflow.

The goal is evidence-led improvement, not guaranteed placement.

01 Select synthesis prompts Start with stable commercial and category questions.
02 Map claim evidence Preserve answer mode and source context.
03 Repair ambiguity Improve facts, structure and third-party support.
04 Review answer depth Run the same measurement set and compare changes.
Related

Keep exploring

Product Explore the complete platform See how measurement connects to content and distribution. Open resource → Tool Run an AI Visibility Snapshot Get a limited sample before creating a formal project. Open resource → Guide Build a GEO measurement framework Define prompts, markets, evidence and review cadence. Open resource →
FAQ

Questions about GEO for Claude

Can Claude use web search?

Claude supports server-side web search in supported product and API experiences. Availability and citation behavior can vary by configuration.

What evidence works well for long-form synthesis?

Clear methodology, stable primary documents, precise definitions and internally consistent product facts make synthesis easier to verify.

Should Claude be measured with the same prompts as ChatGPT?

Keep a shared core prompt set for comparison, then add engine-specific prompts for tasks where long-form analysis or document synthesis matters.

How often should Claude be re-measured?

Often enough to separate a real change from sampling variance. A single run is a baseline, not a trend.

Why does Claude name a competitor instead of us?

Usually because the evidence it can read about them is clearer or more corroborated. That gap is what the knowledge base and content work are for.

Do results from Claude transfer to other engines?

Partly. The underlying evidence is shared, but retrieval behaviour and citation formats differ, so each engine is still measured on its own.

From signal to action

Build a measurable Claude visibility program.

Create a project, confirm your market and start measuring the questions that matter.

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