Detailed Report:

Discoverability

❌ Image or video discovery support not found

What we saw

We didn’t find a dedicated way for images or videos to be surfaced alongside the rest of the site’s content. In practice, this means those assets aren’t being clearly packaged up for discovery.

Why this matters for AI SEO

Generative engines rely on clear, crawlable signals to find and understand different content types. When media assets aren’t easily discoverable, they’re less likely to be used as supporting evidence or referenced in answers.

Next step

Add a clear discovery path for key image and/or video content so those assets can be found and understood alongside your core pages.

Structured Data

❌ Blog/resource markup couldn’t be confirmed

What we saw

A blog/resource page wasn’t available for review, so we couldn’t verify whether that content includes the structured details that help systems interpret articles. As a result, this area shows up as missing in the evaluation.

Why this matters for AI SEO

When article-style content isn’t clearly labeled and described, AI systems can have a harder time extracting “what this is,” “who it’s for,” and “why it’s credible.” That can reduce how often the content is pulled into summaries and recommendations.

Next step

Ensure your blog/resource templates consistently include clear article-level structured details so each post is easy to interpret and trust.

❌ Author identity on blog content wasn’t verifiable

What we saw

Because the resource/blog content wasn’t available to review, we couldn’t confirm that posts have a clear, non-generic author identity presented in a consistent way. This leaves author attribution effectively unverified in the report.

Why this matters for AI SEO

AI-driven search tends to lean on author clarity as a trust cue, especially for advice-oriented content. If author identity is missing or unclear, the content may be treated as less reliable.

Next step

Standardize author attribution for blog posts so it’s consistently clear who wrote the content.

❌ Author credibility links weren’t present/confirmable

What we saw

We couldn’t confirm any supporting author credibility links tied to blog authors because the resource/blog page wasn’t provided for evaluation. That means there’s no visible way, in the reviewed data, to connect authors to established profiles.

Why this matters for AI SEO

When systems can’t connect authors to consistent external identity signals, it’s harder to build confidence in expertise and legitimacy. That can limit how confidently content is reused in AI answers.

Next step

Connect blog authors to consistent public profiles so their identity and credibility are easy to reconcile.

AI Readiness

❌ Major AI crawlers are explicitly blocked

What we saw

We saw explicit blocks in place for major AI crawlers, including Google-Extended and CCBot. In other words, some of the systems that help power generative results are being told not to access the site.

Why this matters for AI SEO

If important AI crawlers can’t access your pages, your brand and content are less likely to be understood, cited, or surfaced in AI-generated responses. This can suppress visibility even when the on-site content is strong.

Next step

Review and update your crawler access rules so key AI crawlers are allowed to read the site.

❌ No Wikidata entity found for the brand

What we saw

We didn’t find a Wikidata entity tied to the brand in the data provided. That leaves a major identity reference point missing.

Why this matters for AI SEO

Generative systems often use public knowledge sources to confirm that a brand is real, consistent, and notable. Without that anchor, it’s harder for AI to confidently connect your brand name, site, and real-world identity.

Next step

Create and/or validate a Wikidata entry that clearly represents the brand and matches your official identity.

Performance

❌ Main homepage content takes too long to appear

What we saw

The primary content on the homepage takes a long time to fully show up for users. That creates a noticeable delay before visitors can actually see the core message.

Why this matters for AI SEO

Slow-loading primary content can reduce engagement signals and make it harder for systems to reliably capture the page’s main point. Over time, this can limit how effectively the homepage supports brand understanding and discovery.

Next step

Prioritize reducing the time it takes for the homepage’s main content to render so the key message shows up promptly.

Reputation

❌ Brand recognition signals were not present

What we saw

We didn’t see the brand recognized across major AI knowledge sources in the reviewed data. This makes the brand harder to validate as a known, established entity.

Why this matters for AI SEO

When AI systems can’t confidently recognize a brand, they’re less likely to reference it in answers or treat it as authoritative. That often shows up as weaker visibility for branded and non-branded queries.

Next step

Strengthen the brand’s presence in commonly referenced knowledge sources so AI systems can more consistently recognize it.

❌ Official brand identity wasn’t verifiable

What we saw

Key identity fields needed to confirm the brand’s official name, website/domain, and address weren’t consistently available in the reviewed packet. That prevents a clean “this is the same entity everywhere” read.

Why this matters for AI SEO

Generative systems place a lot of weight on identity consistency to avoid citing the wrong company or mixing brands. If those anchors aren’t easy to reconcile, the brand may be treated more cautiously.

Next step

Make sure the brand’s core identity details are consistently represented and easy to confirm across major public and third-party sources.

❌ Wikidata coverage and identity anchors were missing

What we saw

A matching Wikidata entity wasn’t found, and there were no official identity anchors available there in the reviewed data. That leaves a gap in one of the most common entity-reference layers.

Why this matters for AI SEO

Wikidata can act like a “connective tissue” between your brand name, site, and other trusted references. Without those anchors, AI systems have less to corroborate when deciding whether to cite the brand.

Next step

Establish a Wikidata presence that includes clear official identity anchors aligned with your brand.

❌ Third-party reviews and customer feedback weren’t found

What we saw

We didn’t see consistent third-party review or customer feedback data in what was provided, and we also couldn’t confirm concrete review sources. That makes reputation hard to validate externally.

Why this matters for AI SEO

Independent customer feedback helps AI systems gauge real-world trust and service quality. When those signals aren’t visible, the brand can look less established compared to competitors with clearer public proof.

Next step

Build and surface verifiable third-party customer feedback signals so they’re easy for systems to reference.

❌ Independent coverage wasn’t present

What we saw

We didn’t find independent offsite coverage (like third-party write-ups or mentions) in the reviewed data, and we also didn’t see owned press/press releases represented. This leaves a thin footprint beyond the site itself.

Why this matters for AI SEO

AI systems tend to trust brands more when there are independent references that corroborate what the company claims. Without that external context, it’s harder to build authority signals that travel beyond your own pages.

Next step

Develop a stronger footprint of credible third-party and/or owned press-style references that can be independently corroborated.

❌ Social profile consensus wasn’t verifiable

What we saw

We didn’t see a consistent, reconciled set of major social profiles confirmed across sources in the reviewed data. That makes it harder to pin down which profiles are the definitive ones.

Why this matters for AI SEO

Official social profiles often function as identity confirmation for brands. If AI systems can’t confidently identify the canonical profiles, it can weaken trust and increase ambiguity.

Next step

Ensure your official social profiles are consistently referenced and easy to confirm as the canonical accounts.

❌ Negative sentiment checks were inconclusive

What we saw

Some reputation-related fields were missing or malformed in the reviewed trust packet, including items related to negative client and employee assertions. Because of that, the report couldn’t confidently confirm a clean read on those areas.

Why this matters for AI SEO

When sentiment and reputation context can’t be clearly verified, AI systems may default to caution rather than confidence. That can reduce how strongly the brand is recommended or described.

Next step

Make sure reputation and sentiment signals are consistently represented in the brand’s public footprint so they can be validated.

LLM-Ready Content (Blog Analysis)

Heads up: this section looks at one article as a snapshot, so it’s a little more interpretive than the rest of the report and may shift slightly from run to run. Have questions? Just shoot us an email at hello@v9digital.com

❌ No non-social outbound references

What we saw

We didn’t see any outbound links that point to non-social, third-party sources. The only outbound destinations detected were social profiles.

Why this matters for AI SEO

Third-party references help AI systems validate claims and understand where information is coming from. Without them, content can be harder to corroborate and reuse confidently.

Next step

Add a small number of relevant third-party references where they naturally support the article’s key points.

❌ Sections are too thin to build strong context

What we saw

The content is broken into sections, but the sections are very short on average, which makes the page feel fragmented. As a result, each segment provides limited standalone context.

Why this matters for AI SEO

AI systems tend to extract meaning section-by-section, and thin sections can make it harder to capture complete ideas. That often reduces how well content can be summarized or quoted accurately.

Next step

Expand key sections so each one provides enough context to stand on its own.

❌ No table-based structure detected

What we saw

We didn’t find any table-based formatting in the article. That means there’s no structured comparison or quick-reference block that can be cleanly lifted.

Why this matters for AI SEO

Tables are a straightforward way for AI systems to interpret and reuse structured facts, comparisons, and checklists. When they’re absent, important details can be harder to extract reliably.

Next step

Include a simple table where it fits (for example, a comparison, checklist, or quick “at a glance” breakdown).

❌ Key answers don’t show up early in sections

What we saw

Within each section, the opening paragraph didn’t provide an immediate, substantive setup. That makes readers (and extractors) work harder to quickly understand the point of the section.

Why this matters for AI SEO

Generative systems often prioritize content that states the “answer” or takeaway upfront. When sections delay the core point, the page can be less likely to be pulled into direct-answer style results.

Next step

Rewrite section openers so the first paragraph quickly establishes the main takeaway before diving into details.

Does Anything Seem Off?

Thanks for taking our free GEO Grader for a spin. When we started this journey, the tool had a fairly long processing time to check everything we wanted both onsite and offsite, so we made a few adjustments on the backend to speed things up. As a result, there are times when the grader may not get everything 100% right. If something feels off, we recommend running the tool a second time to confirm the results. From there, you’re always welcome to reach out to us to schedule a GEO consultation, or to have your SEO provider validate the findings with a more detailed crawl and manual review.