Detailed project record / October 13, 2026
AI-search visibility
Audit, implementation and a measurement plan for Austin AI Hub
01 / Scope
Make organization information consistent and readable
I worked on the Hub website and GEO, combining an audit of its online presence with content and technical changes. The engagement was pro bono.
02 / Implementation decision
Start with verifiable facts and access
The work prioritizes consistent organization details, factual About content, direct-answer FAQs and machine-readable entity information. Crawler access, sitemap and canonical-tag work address how the content can be discovered.
This approach builds an inspectable information foundation. Implementation alone does not establish that an AI engine will cite the organization or that traffic will increase.
03 / Work register
What was addressed
| Area | Documented work | What it can establish |
|---|---|---|
| Entity consistency | Standardized details and corrected inconsistent information. | A clearer account of the organization. |
| Structured data | Organization and FAQ JSON-LD. | Machine-readable descriptions. |
| Technical discovery | robots.txt, sitemap and canonical-tag work. | Access and discovery configuration. |
| Factual content | About content and direct-answer FAQs. | Readable explanations and answers. |
| External presence | Reviewed entity profiles and authority signals. | An audit of consistency beyond the website. |
04 / Public artifact
Inspect the live implementation
Austin AI Hub’s public website ↗ is the implementation example. Its rendered pages and public source can be inspected. The public implementation is the available artifact.
05 / Measurement decision
Keep citations, accuracy and traffic separate
A comparable historical citation and traffic baseline is unavailable. A proposed next step is a dated discovery-prompt set repeated across named AI engines, with the same scoring rules and recorded model versions.
| Measure | Record |
|---|---|
| Organization mentions | Whether the organization appears for the selected prompt. |
| Citations | Which public source is cited and whether it supports the answer. |
| Factual accuracy | Whether the response matches verified organization information. |
| Website traffic | An independently tracked website metric, rather than inferred from citations. |
This is the next measurement step; a comparable citation and traffic baseline is not yet available.
06 / Context
Contribution and boundaries
The contribution described is audit, content and technical work. Austin AI Hub and EDRON AI share leadership. Future observations should record this context, distinguish implementation from impact and avoid causal claims without supporting comparisons.