Case study · Local service business
NY Junk Pros
The launch connected indexable local-service content with observed AI discovery and a ChatGPT-sourced inquiry in the first month.
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Service-area relevance and proof were fragmented across the site, limiting extractability and consistent attribution.
What we changed
- Mapped service and location relationships
- Rebuilt decision-focused landing content
- Added structured entity signals and contextual links
- Separated bots from human referrals in measurement
What we verified
- Before/after page captures
- Search and crawl logs
- AI answer capture
- Lead-source record
Context
The result is a documented sequence for one business and period. It is not presented as a universal uplift claim.
Public evidence layer
Inspect the published work and its boundaries.
This register exposes the public implementation evidence available for this case. Private lead records are summarized only to the level required to support the outcome.
Public page capture
Production service-area contentShows the visible service, borough, operating-capacity and booking information used in the implementation.
Before / after review
Content and structureConfirms that decision-stage service and location evidence moved into readable, indexable page sections.
Attribution boundary
Private record reviewedThe enquiry path was verified in first-party records; contact details and raw analytics remain private.
Publication rule: no contact details, raw analytics exports or confidential client records are exposed. The register states both what each artifact supports and what remains private.
Clearer services, stronger answer-ready content and a measurable route to commercial action.
This case is presented with the level of detail currently approved for publication. It shows the work and observed outcome without turning a single project into a promise for every brand.