We Audited 59 Albuquerque Business Websites. Here Is What the Scan Found.

We scanned 59 Albuquerque business websites with DeepAudit, the browser-based auditor we built and use on client work.

Where they came from: we queried the OpenStreetMap Overpass API for businesses within a 16-kilometer radius of the Albuquerque city center that publish a website, dropped national chains and aggregators, deduplicated the domains, and scanned the surviving homepages. Sixty candidates, 59 complete audits. It is the largest of the city samples in this project.

This is a purposive sample, not a census of Albuquerque and not a random one. It describes these 59 sites, and every percentage counts hard failures only.

The median score was 64 out of 100, the lowest median of any city we scanned. Fourteen sites landed below 40, which is DeepAudit's critical tier, not an industry definition of a broken website. Thirty-five of the 59 scored below 70. None reached 90.

Those numbers describe performance inside our scoring model. They do not tell us who ranks, who gets traffic, or whose website produces leads.

The median mattered less than the repetition. The same findings kept appearing.

Content length and structured data

Forty-two of the 59 homepages fell below DeepAudit's content threshold, which is 300 words.

That is a screening flag, not proof a page cannot rank. A 150-word contact page can be complete. A 1,500-word service page can still be generic and say nothing a competitor does not. The threshold marks pages worth reading, and the read should ask whether the page explains what the business does, who it serves, where it operates, and what to do next.

Thirty-eight sites had no JSON-LD structured data, the highest absence rate of the cities we sampled. Organization or LocalBusiness markup makes the name, address, hours, and service area explicit instead of leaving them to be inferred. It should describe what a visitor can already verify on the page and agree with the Google Business Profile. It does not power the map pack, and it does not guarantee a local ranking. Google says local results turn mainly on relevance, distance, and prominence.

Accessibility findings were nearly universal

Fifty-eight of the 59 pages triggered at least one accessibility failure.

Thirty-seven had a missing or inadequate visible focus indicator, which is what tells a keyboard user which link, button, or field is currently active. Remove it, or make it indistinguishable from the background, and navigating the page gets hard.

This is where manual keyboard and screen-reader testing should start. An automated scan finds detectable problems. It cannot certify a website as accessible or inaccessible.

llms.txt was absent on 43, and that is not an AI-visibility score

Forty-three of the 59 sites had no llms.txt file.

That is file presence and nothing more. llms.txt is a proposed convention, not an established protocol, and there is no documented guarantee that ChatGPT, Claude, or Perplexity consult it when recommending a local business. Our later research found most of these files are generated automatically by a platform or plugin rather than written by the business, which means presence overstates deliberate adoption anyway.

This scan checked whether a file existed. It did not ask an AI assistant to recommend an Albuquerque plumber, so it cannot tell you who gets named.

Headings, metadata, and previews

The primary heading failed on 36 sites, and that number needs a caveat about our own scanner. The rule fails a page for having no H1 *or* for having more than one. Multiple H1s are not automatically an SEO defect, so this count is stricter than it should be. The honest version: 36 pages have a heading structure worth reviewing, and some of them are fine. We are fixing the rule.

Thirty-two sites had no meta description. Google generates snippets from page content and may use your description when it summarizes the page better, so writing one gives you influence over the presentation, not control of it.

Thirty-one lacked complete Open Graph metadata. That reduces your control over the title, image, and description a platform shows when someone shares the link. Some platforms will still build a preview from other page content, so the link is not "broken," it just looks like whatever the platform decides.

What this sample tells us, and what it does not

The same content, structured-data, accessibility, heading, and metadata findings recurred across 59 sites. Correcting them produces clearer pages, more explicit business information, and fewer barriers for visitors.

What the scan does not establish: that these businesses are invisible, that Albuquerque is easy to win, or that fixing a scanner's checklist moves anyone from the bottom of the market to the top. A low median in our model says the sampled pages triggered a lot of rules. It says nothing about search demand, review counts, incumbent brands, proximity, ad spend, or how hard any specific query actually is. We did not measure any of that.

The scan tells you where to look. You still have to decide what it means.

Check your own site

Run the free audit on your own site. No signup, no credit card, and the report is yours to keep.

Then verify it. When a report calls a page thin, read the page. When it flags a heading, look at the heading. When it finds no schema, compare your visible business details against your Google Business Profile before writing any markup. The tool produces evidence, and someone still has to decide what is true.

If you would rather we read it with you, book a free 15-minute teardown and we will walk through what it found, live, no pitch.