The solution
What exactly is an AI-readable review site?
The short answerA site on a domain you own that publishes every one of your reviews as structured data rather than as decoration — each one read and scored for service, problem, person and sentiment, typed in schema.org for your specific trade, and exposed through a live endpoint a model can query directly instead of scraping.
Why a separate site rather than a page
Because the record has to be exhaustive to be useful, and exhaustive is the opposite of what a sales page should be. Two hundred analysed reviews, per-service breakdowns and per-technician summaries would ruin your homepage and are exactly what makes the record valuable.
Giving it its own domain also means it can be built entirely for machine reading — every URL predictable, every fact declared, nothing loaded by a script after the fact.
- Every review, with author, date, rating and full text — not a curated selection
- Each review analysed: which service it refers to, which problem was solved, who was named, what the sentiment was
- Per-service and per-problem pages, so a specific question reaches a specific answer
- Per-technician summaries, because named people are among the strongest trust signals in the corpus
- Aggregate ratings, service areas, hours and contact details as typed schema.org data
- An editorial summary of the corpus, written to be quotable
- A lead form and a phone number that go directly to you
The part most people have not seen
Alongside the pages, the site publishes a live MCP endpoint and an ai-plugin manifest. That means an assistant can call your record and ask it a question — search the reviews for a keyword, get the sentiment on punctuality, look up a technician — rather than hoping it scraped a useful page at some point.
You can try this on a real one. Every site we run exposes it openly, no key required.
How it gets built
We pull your public reviews, read every one with a language model, and extract the structure — services, problems, people, sentiment per aspect. That becomes a dataset rather than a wall of text.
Then the site is generated from that dataset on a domain registered for you, submitted to the indexes that accept submissions, and connected to a weekly probe that asks the major models questions about your business and records the answers.
What you do: confirm the listing is yours. That is the whole of your involvement.
Answers come from this site. Anything it can’t answer goes to Gus.
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