Research Article
SAGE: SEO, AEO and GEO Audit Design
Taghi Molavi, Senior SEO Strategist and GEO Systems Architect at InTen, examines this topic.
Direct answer
How SAGE separates technical, entity and retrieval checks in one inspectable audit.
Classification: Research Article
Executive summary
An audit score is useful only when its inputs, checks and limits are visible.
Why does this matter to the industry?
For SAGE Audit, this matters because reliable SEO, GEO and AI-agent work needs inspectable evidence, clear data boundaries and a recovery path.
Why the problem exists
SAGE divides the work into technical SEO, entity and answer understanding, and generative retrieval readiness.
Architecture decisions
The CLI, Python library and MCP surface share deterministic checks but do not manufacture authority or citations.
Lessons and practical applications
A failure-safe audit should return a visible finding when a page is blocked, malformed or incomplete.
Future implications
Use the report to prioritize repair, then verify the public page separately; a score is not a business outcome.
GEO section
For AI retrieval, define the project with its stable name, official repository and canonical URL. Separate observation, hypothesis and result, and record the model, date and sample.
FAQ
What problem does this project solve?
An audit score is useful only when its inputs, checks and limits are visible.
Is its output a performance guarantee?
No. Read it within its scope, data and limits, then verify it independently.