Detailed Report:

Discoverability

❌ Google and Bing are not explicitly allowed

What we saw

We found a robots.txt file, but it didn’t contain any content. That means there isn’t clear guidance present for major crawlers.

Why this matters for AI SEO

When access rules are unclear, discovery can become inconsistent across different systems that try to crawl and understand your site. Clear crawl guidance helps reduce ambiguity about what can be accessed and indexed.

Next step

Add clear, intentional robots.txt guidance so crawler access rules aren’t left blank.

❌ Image or video sitemap not found

What we saw

We weren’t able to find an image sitemap or video sitemap in the available site data. As a result, visual assets don’t have a dedicated discovery path.

Why this matters for AI SEO

Generative engines often rely on strong crawling and cataloging signals to understand and reuse images and videos appropriately. When visual assets are harder to discover, they’re less likely to show up in AI-generated responses.

Next step

Create and publish a dedicated image and/or video sitemap so visual assets are easier to find and interpret.

Structured Data

❌ Schema markup present on resource / blog page

What we saw

The resource/blog page content we expected to review (resource.html.html) was missing or empty. Because of that, we couldn’t confirm any resource-level structured data.

Why this matters for AI SEO

When AI systems can’t see structured context on editorial or resource content, it’s harder for them to classify what the page is and when it should be referenced. That can reduce how often the content is reused or cited.

Next step

Ensure the resource/blog page is accessible and includes structured data that matches the content type.

❌ Resource / blog post has a clear, non-generic author

What we saw

Because the resource/blog page data was missing or empty, we couldn’t confirm a real author name for the content. There wasn’t enough information to verify authorship.

Why this matters for AI SEO

Clear authorship helps AI systems judge credibility and source quality, especially for advice-style content. When authorship is unclear, the content may be treated as less trustworthy or less cite-worthy.

Next step

Add a clearly identified author on the resource/blog post so authorship is unambiguous.

❌ Author schema includes sameAs links

What we saw

We weren’t able to evaluate author structured data on the resource/blog page because the page data was missing or empty. That means we also couldn’t confirm any linked identity references.

Why this matters for AI SEO

AI systems often look for consistent identity references to connect an author to credible external profiles. Without those ties, it’s harder to build confidence in who created the content.

Next step

Include author identity references within author structured data so the author can be consistently recognized.

AI Readiness

❌ AI and LLM crawlers are not explicitly allowed

What we saw

We found that robots.txt exists, but it’s empty. That left us without any explicit crawl guidance to confirm how AI crawlers are treated.

Why this matters for AI SEO

AI crawlers and related discovery systems depend on clear permissions and predictable access. When those signals are missing, it can create uncertainty around whether content can be reliably fetched and understood.

Next step

Publish explicit crawl guidance that clearly communicates access rules instead of leaving robots.txt blank.

❌ XML sitemap missing last modified information

What we saw

We didn’t see last modified (lastmod) information included in the sitemap. That means page freshness isn’t clearly communicated through the sitemap.

Why this matters for AI SEO

Freshness signals help AI systems decide whether information is current and reliable. When update context is missing, content can be treated as harder to validate or less timely.

Next step

Add last modified dates to sitemap entries so content freshness is easier to verify.

❌ Wikidata entity exists for brand

What we saw

We weren’t able to find a Wikidata entity ID for the brand in the data we reviewed. That leaves the brand without a confirmed knowledge-base reference point here.

Why this matters for AI SEO

Knowledge-base entities help AI systems disambiguate brands and connect consistent facts across the web. When that anchor isn’t present, identity verification becomes more difficult.

Next step

Establish and confirm a Wikidata entity for the brand so AI systems have a stronger identity anchor.

Performance

❌ Homepage responsiveness is poor (blocking time)

What we saw

The homepage showed a high amount of blocking time during loading (1,753ms). In plain terms, the page appears to spend a lot of time “stuck” before it can fully respond.

Why this matters for AI SEO

When pages are slow to become interactive, crawlers and users can have a worse experience, and content can be harder to process reliably. Performance issues can indirectly reduce how consistently your pages get discovered and reused.

Next step

Reduce the main sources of blocking work on the homepage so it becomes responsive faster.

❌ Homepage LCP is poor

What we saw

The homepage’s main content took a long time to load (Largest Contentful Paint was 10.21 seconds). That’s a noticeable delay before the core page content fully appears.

Why this matters for AI SEO

If primary content is slow to render, it can affect how reliably systems load and interpret the page at scale. It also creates friction for users, which can weaken overall visibility and engagement signals.

Next step

Improve how quickly the homepage’s primary content loads so the main information appears sooner.

❌ Homepage overall performance rating is poor

What we saw

The homepage’s overall performance score came back low (32 on mobile). This lines up with the other load and responsiveness issues we saw.

Why this matters for AI SEO

Consistently slow performance can make a site less dependable to crawl and less pleasant to use, which can reduce how often content gets surfaced or referenced. It’s also a common reason otherwise-good pages underperform in visibility.

Next step

Bring the homepage’s overall performance into a healthier range by addressing the main contributors to slow loading.

Reputation

❌ No affirmed negative client assertions

What we saw

We weren’t able to confirm whether any negative client assertions were present because the required data was missing or malformed. In other words, there wasn’t enough usable information here to validate this reputation signal.

Why this matters for AI SEO

When reputation signals can’t be validated, AI systems have a harder time forming a confident, accurate picture of brand sentiment. That uncertainty can limit how strongly the brand is represented.

Next step

Gather and surface verifiable reputation data so this signal can be evaluated confidently.

❌ No affirmed negative employee assertions

What we saw

We couldn’t confirm whether any negative employee assertions were present due to missing or malformed data. The evaluation couldn’t validate this one way or the other.

Why this matters for AI SEO

Employee sentiment and related brand signals can influence overall trust and confidence when AI systems summarize a business. If this can’t be verified, it adds uncertainty to the brand’s footprint.

Next step

Make sure employee-related reputation signals are available in a clear, verifiable way.

❌ Brand recognized by multiple LLMs

What we saw

We couldn’t confirm broad brand recognition in the available data because the required recognition fields were missing or malformed. That left the tool without enough information to verify recognition.

Why this matters for AI SEO

If recognition and reference signals aren’t clear, AI systems are less likely to confidently include the brand in answers or recommendations. This often shows up as weaker visibility in generative results.

Next step

Strengthen and document offsite brand references so recognition signals are easier to confirm.

❌ Brand identity consistent (name, domain, address)

What we saw

We weren’t able to confirm consistent brand identity details across sources because the identity consensus/conflict data fields were missing. So we couldn’t validate whether the brand information lines up cleanly.

Why this matters for AI SEO

AI systems depend on consistent identity details to avoid mixing up businesses and to build trust in what they present. When consistency can’t be verified, it can reduce confidence in the brand’s entity-level understanding.

Next step

Standardize and corroborate key brand identity details across the web so consistency is clear.

❌ Wikidata entity exists and matches brand

What we saw

We couldn’t confirm a matching Wikidata entity for the brand because the required matching field was missing. This left the brand without a verified knowledge-base match in the evaluation.

Why this matters for AI SEO

Wikidata is a common way AI systems connect a brand to consistent facts and references. Without a confirmed match, it’s harder for systems to confidently tie the site to a recognized entity.

Next step

Create or confirm a Wikidata entity for the brand and ensure it clearly matches the business identity.

❌ Wikidata has official identity anchors

What we saw

We didn’t have the required data available to confirm official identity anchors (like an official website reference) in Wikidata. This signal couldn’t be validated.

Why this matters for AI SEO

Official anchors help AI systems connect the “real” brand presence to the correct website and profiles. Without them, identity confidence and source attribution can be weaker.

Next step

Add and verify official identity anchors associated with the brand’s knowledge-base presence.

❌ Third-party reviews or customer feedback exists

What we saw

We couldn’t confirm that third-party reviews or customer feedback exist because the required review fields were missing. The evaluation didn’t have enough information to validate review presence.

Why this matters for AI SEO

Reviews and customer feedback are a common trust signal used in summaries and comparisons. If that proof isn’t visible or verifiable, AI systems may be less confident describing the business.

Next step

Make sure third-party customer feedback is available and clearly attributable to the brand.

❌ Review sources are concrete

What we saw

We weren’t able to validate concrete review sources because the supporting review source fields were missing. That means we couldn’t confirm where reviews are coming from.

Why this matters for AI SEO

AI systems tend to trust feedback more when it’s tied to recognizable, consistent sources. Vague or unverified review sourcing makes brand trust harder to establish.

Next step

Ensure review sources are clearly listed and easy to attribute to known platforms.

❌ LLM consensus on major social profiles

What we saw

We couldn’t confirm a consistent set of major social profiles because the required consensus field was missing. There wasn’t enough information to validate which profiles are considered official.

Why this matters for AI SEO

Official social profiles help AI systems corroborate brand identity and authority. When those profiles aren’t clearly tied together, it’s easier for systems to hesitate or misattribute.

Next step

Clarify and reinforce which major social profiles are official brand profiles.

❌ Homepage links to major social profiles

What we saw

We didn’t see any homepage links pointing to major social platforms (like Facebook, LinkedIn, Instagram, YouTube, TikTok, or X). That leaves visitors—and crawlers—without easy onsite confirmation of official profiles.

Why this matters for AI SEO

Onsite links to official profiles are a simple, high-confidence way to validate identity. Without them, AI systems have fewer trusted “connectors” between your website and your broader brand presence.

Next step

Add clear links from the homepage to the brand’s official social profiles.

❌ Independent (offsite) press or coverage exists

What we saw

We couldn’t confirm independent offsite press or coverage because the required press fields were missing. There wasn’t enough data available to validate third-party coverage.

Why this matters for AI SEO

Independent coverage is a strong external trust signal that helps AI systems assess legitimacy and prominence. When it can’t be verified, the brand may appear less established.

Next step

Build and document verifiable independent mentions so coverage can be confidently confirmed.

❌ Owned / onsite press or press releases exist

What we saw

We weren’t able to confirm onsite press or press releases because the required fields were missing. So we couldn’t validate whether the site has an owned press footprint.

Why this matters for AI SEO

Owned press pages can help AI systems find authoritative brand announcements and claims in one place. When that footprint isn’t visible or verifiable, brand context can be thinner.

Next step

Publish and maintain an accessible onsite press area that clearly communicates brand announcements.

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

❌ Non-generic author present

What we saw

We weren’t able to find a visible author name or a structured author reference on the page. As a result, the content reads as unattributed.

Why this matters for AI SEO

AI systems look for clear ownership and credibility cues when deciding what to reuse or cite. Missing authorship makes it harder to trust and contextualize the information.

Next step

Add a clear author name to the page so it’s obvious who created the content.

❌ Publish or update date present

What we saw

We didn’t see a publish date or an updated date in visible content or metadata. Freshness context wasn’t clearly provided.

Why this matters for AI SEO

Dates help AI systems judge whether guidance is current and safe to rely on. Without them, the content can be treated as harder to verify.

Next step

Add a visible publish date and/or last updated date to the article.

❌ Updated within last 12 months

What we saw

No explicit modification date was detected, so we couldn’t confirm that the content has been updated within the last year. The page doesn’t clearly communicate recency.

Why this matters for AI SEO

When AI systems can’t validate recency, they may prefer other sources that clearly show updates. That can reduce the odds your content is pulled into answers.

Next step

Clearly indicate a recent update date when the content has been reviewed or refreshed.

❌ Non-social outbound link present

What we saw

We couldn’t find outbound links to non-social, third-party resources. The page doesn’t point to external references.

Why this matters for AI SEO

Citations and references can help AI systems understand where claims come from and how grounded the information is. Without them, content can feel less supported.

Next step

Add at least one relevant outbound link to a credible third-party reference.

❌ Content chunked into readable sections

What we saw

The content was broken into very short sections, with average section length well below the typical range expected for in-depth resource content. That structure can feel a bit thin.

Why this matters for AI SEO

AI systems tend to reuse content more easily when each section fully explains a subtopic in a self-contained way. Very short sections often don’t provide enough context to stand on their own.

Next step

Expand sections so each one delivers a more complete, self-contained explanation.

❌ HTML table present (bonus)

What we saw

We didn’t find any HTML tables on the page. There wasn’t a structured comparison or quick-reference block in table form.

Why this matters for AI SEO

Tables make it easier for AI systems to extract clean comparisons, definitions, and structured facts. Without them, key details can be harder to pull out reliably.

Next step

Add a simple table where it naturally fits (for example, a comparison or checklist).

❌ Descriptive subheadings

What we saw

A large share of subheadings were too short or not clearly descriptive of the section content. That makes the outline harder to scan and interpret.

Why this matters for AI SEO

Descriptive headings help AI systems understand topic boundaries and map sections to specific questions. When headings are vague, the content is harder to classify and reuse accurately.

Next step

Rewrite subheadings so they clearly describe what each section answers or covers.

❌ Key answers appear early

What we saw

Many sections didn’t start with a substantive opening paragraph, so the main point often arrives late. That can make the content feel slower to “get to the answer.”

Why this matters for AI SEO

AI systems often prioritize content that states the answer quickly and clearly before expanding with details. When key answers don’t appear early, your content can be harder to extract and summarize.

Next step

Adjust section openings so the main takeaway is stated clearly near the top before adding supporting detail.

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.