How the audit actually works

Your overall lead-readiness score is a weighted combination of the two dimensions we score directly today: AI search visibility and mobile speed. Technical health adjusts that score when we find problems that get in a crawler's way. Local presence is coming soon and doesn't affect the score yet. The weights reflect our judgement about what costs a trades business the most calls, not a law of nature.

We run live queries against ChatGPT with web search, Perplexity and Google Gemini, phrased the way a homeowner would actually phrase them, for your trade and your service area. We record which businesses each assistant names, in what order, and what sources it cites.

The gap between who gets recommended and whether you're on that list is the finding. We show you the exact queries where competitors are named and you aren't, and the sources each assistant cited.

Mobile speed. We measure your site's mobile loading speed with Google PageSpeed and report whether the numbers come from lab estimates or real-visitor data, so you know exactly what was measured.

Technical health. We check whether search engines and AI can cleanly read your pages — for example, the same page served at two different web addresses, which can split your ranking. When a site blocks our automated check, we say so plainly rather than guessing.

Local presence. Whether you show up in local search and your Google Business Profile lines up with your website. This isn't part of the automated audit yet — it's coming soon.

A measurement of a website and its listings, taken on a specific day. It is not a prediction of revenue and it is not a grade of your workmanship. Plenty of excellent trades businesses score badly here, which is the entire reason the product exists.

This is the section most audit tools leave out.

Why an AI chose someone. We can see who got named and what evidence they have. We cannot see inside the model. Nobody outside those companies can. When we say a factor is associated with being recommended, we mean we observed it on the businesses that were recommended, not that we proved it caused the recommendation.

What happens next week. AI answers are not deterministic. Run the same query twice and you can get two different answers. Platforms change their models and their rules without notice. A finding is true when we ran it.

Whether fixing it will work. Closing an evidence gap makes your business easier to verify and easier to cite. It does not guarantee anyone will cite it. We do not promise rankings, leads or revenue, and you should be suspicious of anyone who does.

Sample size. We test a set of representative queries, not every query a homeowner could type. A different phrasing can return a different set of names.

Any figure we publish has to come from a named, checkable source, or from our own audit data. If we cannot point at where a number came from, it does not go on the site. We removed several statistics under exactly this rule rather than keep claims we could not stand behind.

Where we cite a study, we will tell you whose it is and what it actually measured, including when it measured something adjacent to your situation rather than your situation itself.

During early access, audits are run by hand by the founder. That means slower turnaround (two business days) and more judgement applied to your specific business. When the automated scanner ships, the checks stay the same and the turnaround gets shorter. We will say clearly on the report which one produced it.