Why reviews
Why are reviews the strongest signal an AI has?
The short answerBecause they are third-party, high-volume, timestamped, attributed and specific — and a small business owns almost nothing else that is all five at once. Everything you write about yourself is a claim. A hundred dated reviews from named strangers describing the same thing independently is evidence, and evidence is what a model can act on.
Claims versus evidence
Every business website makes the same claims. Family owned. Twenty years' experience. Quality workmanship. Fully insured. The words are identical across an entire trade, which makes them worthless as a way of telling two businesses apart — to a person, and much more so to a model weighing sources.
Reviews are the opposite in every respect. They are written by people with no stake in your marketing, at moments you did not choose, in language you did not approve, about specific things that specifically happened.
Volume is what makes it hard to fake
A single review proves nothing. Two hundred, spread across four years, mentioning the same technician by name and the same recurring strength, is a pattern that cannot be manufactured and stay consistent.
That consistency is precisely what a model is good at detecting, and it is why review corpora carry weight that a testimonial page never will.
What we extract from them
The reviews themselves are the raw material. What makes them usable is the structure pulled out of each one.
On a real corpus that means: which services get mentioned and how positively; which problems customers arrived with; which technicians are named and how often; and sentiment scored per aspect — punctuality, communication, pricing, cleanliness, problem resolution.
On one live site that analysis produced 1,001 reviews at 97.2% positive, with punctuality at 100% across 205 mentions and pricing at 75.6% across 78. That second number is not flattering, and it is published, because a record that only says good things is one a model learns to discount.
Turning a pile into a record
Every review is read individually — not keyword-matched — and tagged with the services it refers to, the problems it describes, any person it names, and a sentiment score per aspect. That output is aggregated into per-service and per-aspect views and published as structured data alongside the original text.
The original review is never edited, never summarised in place, and never selectively omitted. The structure sits on top of it.
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