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.
References
Related reading
Read What Is AI Visibility?, Request a project review.
What SAGE Audit checks
SAGE Audit separates technical accessibility, entity and answer understanding, and generative retrieval readiness. The separation makes it easier to see whether a problem is caused by crawlability, unclear identity, weak answer structure or an untested retrieval assumption. It produces findings to investigate; it does not manufacture authority or citations.
A simple workflow
- Select a public URL and define the audit scope.
- Run the technical, entity and retrieval-oriented checks.
- Read the finding, evidence and limitation for each check.
- Prioritize a repair and apply it through the normal change process.
- Re-crawl the public page and record what changed.
Package, source and demo
- PyPI provides the installable release: SAGE Audit on PyPI.
- GitHub provides the source, issues and implementation details: SAGE Audit on GitHub.
- Hugging Face provides a public Space for quick testing: SAGE Audit on Hugging Face.
The Space may demonstrate a workflow, replay or simulation. Unless the page says otherwise, do not describe it as a live model or live web-search audit. The package and repository are the right places to inspect the full implementation and versioned behavior.
Who should use it?
Technical SEO teams, content engineers, researchers and developers who need structured audit findings can use SAGE Audit as a starting point. It is not a substitute for a production change-control process or independent verification.
Boundaries
SAGE Audit cannot guarantee rankings, AI visibility, citations or revenue. A passing check is not proof that an AI system will retrieve or recommend a brand. Interpret each result within the URL, configuration, date and evidence available.
Official profiles and next step
See Molavi.pro, the GitHub profile, PyPI profile and Hugging Face profile. If you want to turn findings into a scoped technical plan, request a project review.
SAGE Audit is developed by Taqi Molavi and the Molavi.pro ecosystem and is published for public use, inspection and contribution through PyPI, GitHub and Hugging Face.