AI-visibility content engagement for a garden center

01

The problem

A thirty-year-old local business ranks well in classic search and barely registers in AI answers, where its best pages are cited individually but invisible at the topic level. The owner is rightly afraid that any change to a ranking page will cost traffic.

02

What I did

Pulled the organic, competitor, and AI-visibility baselines through the Semrush MCP connector, built a branded strategy site from the data, and wrote proposals sized to the research-supported threshold for moving the metric rather than to the easiest yes.
Ran the content work as a pipeline, not a document: every article has a concept, research, brief, LLM draft, fact-check, readiness check, schema, build log, and publish log, and nothing changes a ranking page without explicit approval. Reviewers comment and vote in a Cloudflare Access-gated portal backed by D1.

One article through the pipeline

Concept & research Search and AI-visibility data through the Semrush connector.
Brief Intent, claims to make, sources to cite.
LLM draft Written to the brief, not from scratch.
Fact-check Every claim traced to a source or cut. gate
Readiness & schema Structured data and a readiness check against the brief.
Client review Reviewers comment and vote in an Access-gated portal. gate
Publish log Nothing changes a ranking page without approval on record.

03

Outcome

A repeatable engagement template, from baseline to measured publication, that is now the product Aceleca sells.

04

In the stack

Where it sits

The highlighted nodes are the systems and people this project touched.

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