Why We Built DeepAudit AI

We wanted to open a sales call already knowing what was broken on the prospect's website. Not with generic SEO talking points, but with a specific URL, a specific failed check, and something the owner could open in a second tab and see for themselves.

That turned out to be harder to get than we expected, and it is the whole reason DeepAudit exists.

What we tried first

We ran prospects through the audit tools that were already on the market. They were slow, partial, or expensive, and often all three. Some covered technical SEO and skipped content. Some covered content and ignored security headers. Very few would crawl a whole site at a price we could justify for a prospect who had not paid us anything yet.

Then we hit the problem that actually decided it. The reports did not match what we saw when we opened the same site in a browser. Pages that looked fine were flagged for missing headings. Problems we could see with our own eyes went unmentioned. When we chased the disagreements down, the cause was usually the same. The tool had read the HTML the server sent. We had been looking at the page the browser built after JavaScript ran.

Those are two different documents, and neither one is automatically the real page. We wrote that argument out in full in Raw HTML and the rendered DOM tell different stories, so we will not repeat it here. The short version is that a tool which only ever inspects one stage will make confident statements about the other stage that are not true.

What we built

DeepAudit AI loads the page in Chromium with Puppeteer, waits on a network-idle condition, and inspects the rendered DOM. It runs 60+ checks across nine scoring categories: technical, content, on-page, social, security, performance, accessibility, AI readiness, and locality. A deep scan crawls up to 50 pages. There is no account and no sales call attached to it.

It does not scroll and it does not click. That is a deliberate limit. Google Search does not interact with pages either, so a tool that scrolls and clicks can assemble a version of the page that Google will never build, and then hand you a clean report on content that is not in the index. We would rather be limited the same way our target is limited.

If you want the engineering, that is a separate post: how we built it with Puppeteer and Chromium.

What changed for us

The sales call changed. We stopped saying "your technical SEO could be stronger" and started saying "this page returns a 404 from your own footer, here is the URL." One of those is a conversation. The other is a brochure.

Scoping changed too. A proposal is much easier to write when a crawl has already told you which templates are broken and how many pages sit on each one. We were guessing at scope before. Now we are reading it off a report.

What we found once we started scanning at volume

The first batches were prospect sites we were about to call. A marketing agency had 196 words on its homepage. An SEO agency had no structured data on its own site. One company had internal pages, including services and contact, that timed out during the crawl.

Once we had scanned enough sites to say something with a denominator, we published it. In our 292-site scan, 191 sites returned a valid mobile verdict, and 185 of those 191 failed at least one mobile Lighthouse threshold. The median mobile LCP was 10.5 seconds.

Two limits on that number, because they matter. Those are Lighthouse lab measurements of LCP, FCP, and CLS, which are not the same thing as Google's field Core Web Vitals. And the sample came from a B2B prospect list, so it is purposive, not random. It tells you what we found in that list. It does not tell you the odds for your site.

What DeepAudit is not

It is not Googlebot. No third-party tool is. It loads the page in Chromium and inspects the rendered DOM, which is a useful approximation and nothing more. The only view that tells you what Google actually built is the URL Inspection tool in Search Console.

It also gets things wrong. Our external link checker has counted blocked crawlers as broken links, including on our own site. Our old H1 rule treated multiple H1 tags as a failure, which is not an SEO failure, and that rule inflated failure counts in numbers we had already published. We keep a running account of where our own scanner produces false readings in four ways an audit gives you a false reading, because a tool that cannot name its own failure modes is asking you to trust the score instead of the evidence.

We built DeepAudit because we needed the evidence. We left it free because the fastest way to find out whether a finding is real is to let the person who owns the site go look.