SYA self-audit
We held Send Your Agent to the standard it sells.
The site already had strong agent-readable files. A wider audit found that machine discovery was ahead of search identity, buyer conversion and executive reporting. This release closes those implementation gaps.
Starting strength
Agent-readable foundations
Core discovery files, catalog routes, sitemap, robots and public product APIs were already live.
Audit finding
A 100 score was too narrow
The old score did not test canonical identity, structured data, page-specific social cards or conversion friction.
Release response
Broader evidence, clearer boundaries
The upgrade widens measurement and keeps structural readiness separate from observed answer visibility.
Before and after
Implementation evidence, not a victory lap.
| Area | Before | After this release |
|---|---|---|
| Measurement | A legacy structural score focused mainly on discovery files and crawlability. | Scanner V2 separates discovery, crawlability, search entity, content clarity and commercial trust. |
| Search identity | Audited pages had no self-referencing canonical tags or JSON-LD identity. | Public pages declare route-specific canonicals, social metadata and appropriate Organization, Service, Offer or FAQ schema. |
| Buyer conversion | Pricing and checkout sat below a long introductory sequence, especially on mobile. | Package price, local currency and checkout now appear in the first decision area, with scope and boundaries below. |
| Reporting | The paid audit opened as a Markdown-style text deliverable. | The private workspace presents an executive scorecard, ownership plan, acceptance tests and print-ready report. |
| Visibility evidence | Structural readiness could be mistaken for proof of visibility in AI answers. | The free scan states that visibility is not measured; monitoring records limited search-backed prompt samples separately. |
What this does not prove
Shipping cleaner discovery and conversion surfaces does not prove higher rankings, traffic, mentions, citations or sales. Those outcomes require a meaningful observation window and enough real buyer activity to avoid reading noise as impact.