Overview:
On 10/02/26 bartleypatterson.com scored 52% — **Fair** – Overall, the basics are in place, but a few key signals are either missing or hard for AI systems to confidently confirm.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - The site has a strong technical foundation for discovery, though the absence of image or video sitemaps is a missed opportunity for better visibility.
- Structured Data: 58% - Overall, this section looks to be in good shape regarding the homepage setup, but we weren't able to confirm author-level schema for individual resources.
- AI Readiness: 67% - The site has a solid technical foundation with healthy sitemaps and clear brand context, but it lacks a Wikidata entity to help AI models establish a formal identity.
- Performance: 50% - The homepage is staying responsive and stable, but it's hitting a major wall with loading times for the main visual content.
- Reputation: 12% - The site provides clear links to social media profiles, but we were unable to verify broader offsite signals like brand recognition, Wikidata presence, or independent press mentions in the data reviewed.
- LLM-Ready Content: 60% - The page is technically sound with clear authorship and recent updates, though the section-level detail is a bit thin for optimal AI comprehension.
What stands out most overall
The big picture is that the site is generally understandable, but it’s not consistently backed by the kinds of external validation and content formatting signals that help AI systems feel confident. Most of the gaps read less like “errors” and more like missing clarity around identity, trust, and how information is packaged for reuse. The next section breaks down the specific areas where the evaluation couldn’t confirm key signals, along with the few places where the on-page experience may be holding visibility back. None of this is unusual—it just gives you a clean shortlist of what’s most limiting right now.
Detailed Report
Discoverability
❌ Image or video sitemap not found
What we saw
We didn’t find an image or video sitemap referenced in the sitemap index or at the expected locations. That makes it harder to confirm that rich media assets are being consistently surfaced for discovery.
Why this matters for AI SEO
AI-driven experiences often pull in visual assets when they can reliably find and interpret them at scale. If media is harder to inventory, those assets are less likely to show up in AI summaries and visual-rich results.
Next step
Add an image and/or video sitemap and make sure it’s referenced alongside your existing sitemap setup.
Structured Data
❌ Blog/resource page markup couldn’t be verified
What we saw
We didn’t have a working blog or resource page to evaluate, so we couldn’t confirm whether those pages include structured data the way the homepage does. In the packet, the resource page content was missing or empty.
Why this matters for AI SEO
When deeper content pages don’t provide clear, consistent context, AI systems have a harder time interpreting what each page is “about” and when to cite it. That can limit how often supporting content is surfaced in AI answers.
Next step
Make sure your blog/resource hub is accessible and includes the same baseline structured context you’re using on core pages.
❌ Blog post author clarity couldn’t be confirmed
What we saw
Because the resource/blog content wasn’t available to review, we couldn’t confirm that individual posts show a clear, non-generic author. This was marked as missing due to the resource page content not being present in the packet.
Why this matters for AI SEO
Authorship is one of the easiest ways for AI systems to judge who is behind content and whether it should be trusted or reused. If author info isn’t consistently detectable, the content can lose credibility in AI-generated summaries.
Next step
Ensure each resource/blog post clearly identifies a specific author (not a generic label) in a way that’s easy for machines to read.
❌ Author profile connections (sameAs) couldn’t be confirmed
What we saw
We couldn’t validate whether author information includes profile references (like consistent identity links) because the blog/resource content wasn’t available to analyze. The evaluation flagged this as missing due to the resource page content being absent.
Why this matters for AI SEO
AI systems are more confident when they can connect an author to a consistent identity footprint across the web. Without those connections, it’s harder for AI to attribute content and build trust in the source.
Next step
Add consistent author identity references on author profiles (and/or in post-level author data) so AI systems can reliably connect the dots.
AI Readiness
❌ No Wikidata entity found for the brand
What we saw
We didn’t find a Wikidata item ID associated with the brand in the trust data provided. In other words, there wasn’t a clear Wikidata entity we could tie back to the site.
Why this matters for AI SEO
AI systems often lean on well-known entity sources to confirm that a brand is real, distinct, and consistently described. When that entity connection is missing, AI may be less confident about identity and authority.
Next step
Create or claim a Wikidata entity for the brand and connect it to your official identity details.
Performance
❌ Main content loads very slowly on the homepage
What we saw
The homepage’s main content took a very long time to fully appear (Largest Contentful Paint was recorded at 24.05 seconds). That’s a meaningful delay for real users trying to engage with the page.
Why this matters for AI SEO
When pages feel slow to load, engagement and crawling efficiency can suffer, which reduces how reliably content gets discovered and reused in AI-driven experiences. Speed issues also make it harder for systems to confidently extract page meaning at scale.
Next step
Prioritize improving how quickly the homepage’s primary content becomes visible to visitors.
Reputation
❌ No confirmed view on negative client feedback
What we saw
We weren’t able to verify whether there are affirmed negative client assertions in the data provided for this evaluation. The packet didn’t include enough reliable third-party context to confirm this either way.
Why this matters for AI SEO
AI systems tend to model trust based on what they can corroborate across sources. If sentiment signals can’t be validated, the brand’s trust picture can look incomplete in AI outputs.
Next step
Make sure there are clear, verifiable third-party sources that reflect client experience in a way AI systems can reference.
❌ No confirmed view on negative employee feedback
What we saw
We couldn’t confirm whether there are affirmed negative employee assertions based on the information available in the packet. This appears to be a data availability/verification gap, not a definitive statement about sentiment.
Why this matters for AI SEO
When AI systems can’t reconcile brand sentiment signals, they may be less confident in describing the business accurately. That uncertainty can reduce how often a brand is recommended or cited.
Next step
Ensure your brand has consistent, verifiable presence in reputable third-party sources that AI systems can use for sentiment context.
❌ Brand recognition across models couldn’t be verified
What we saw
The evaluation couldn’t verify that the brand is consistently recognized across multiple language models based on the data provided. The packet notes gaps and inconsistencies in recognition.
Why this matters for AI SEO
If AI systems don’t reliably recognize the brand, they’re less likely to surface it in answers, comparisons, or recommendations. Recognition is a big part of how “discoverable” a brand feels in generative results.
Next step
Build more consistent, verifiable brand footprints across reputable sources so recognition is easier to confirm.
❌ Brand identity consistency couldn’t be confirmed
What we saw
We couldn’t verify consistent brand identity details (like name/domain/address alignment) using the provided data. The packet also noted conflicting or unreliable identity signals in model responses.
Why this matters for AI SEO
Identity consistency is a foundation for trust in AI outputs—if systems can’t reconcile who the brand is, they’re less confident in citing or summarizing it. That can also increase the chance of incorrect brand details showing up in AI answers.
Next step
Make sure the brand’s core identity details are consistent across major third-party listings and references.
❌ No matching Wikidata entity could be verified
What we saw
This evaluation did not find a Wikidata entity that clearly matches the brand. As a result, there wasn’t a strong external “entity anchor” to confirm identity.
Why this matters for AI SEO
Wikidata is commonly used as a reference point for entity understanding in AI contexts. Without it, it’s harder for AI systems to confidently distinguish and describe the brand.
Next step
Establish a clear Wikidata entity that aligns with the brand’s official identity.
❌ No official identity anchors found in Wikidata
What we saw
Because a matching Wikidata entity wasn’t found, we also couldn’t confirm the presence of official identity anchors there (like validated links that tie back to the brand). This leaves the entity verification layer incomplete.
Why this matters for AI SEO
Official anchors help AI systems resolve ambiguity and reduce misinformation risks. When those anchors are missing, AI tools may hedge or provide inconsistent brand descriptions.
Next step
Ensure the brand’s external entity references include clear, official anchors that point back to the correct website and profiles.
❌ Third-party reviews weren’t verified
What we saw
The data provided didn’t verify the existence of third-party customer reviews or feedback sources for the brand. In this run, we couldn’t confirm review signals beyond what’s on the site itself.
Why this matters for AI SEO
AI systems often look for independent feedback to support trust and credibility. If reviews aren’t clearly detectable, the brand can look less established in AI-driven results.
Next step
Strengthen the brand’s presence on well-known third-party review platforms so those signals are easier to validate.
❌ Review sources weren’t concrete in the available data
What we saw
Even where reviews may exist in reality, the evaluation packet didn’t surface concrete, verifiable sources for them. That makes it difficult to treat reviews as dependable offsite signals in this report.
Why this matters for AI SEO
AI systems favor sources they can name and cross-reference. If review sources aren’t clear, AI is less likely to use them to support recommendations or summaries.
Next step
Make sure review sources are clearly attributable to recognizable platforms and consistently associated with the brand.
❌ Social profile consensus couldn’t be verified
What we saw
While the homepage links out to social profiles, the evaluation couldn’t verify model-level consensus that those are the definitive, authoritative profiles for the brand. This is another area where offsite identity clarity wasn’t confirmed in the packet.
Why this matters for AI SEO
When AI systems can’t confidently identify the “official” profiles, they may avoid citing them or may mix them up with similarly named entities. That can weaken trust and attribution.
Next step
Reinforce the official nature of key social profiles through consistent naming and verification signals across the web.
❌ No independent press or coverage was verified
What we saw
The evaluation didn’t verify independent, offsite press mentions or coverage in the provided data. That leaves a gap in third-party validation beyond social presence.
Why this matters for AI SEO
Independent coverage helps AI systems gauge legitimacy and authority through external references. Without it, the brand can look less “established” in generative results.
Next step
Work toward earning and maintaining independent mentions that clearly reference the brand and website.
❌ No onsite press or press releases were detected
What we saw
We didn’t see evidence of an onsite press area or press releases in the evaluation data. That means there wasn’t an obvious owned source for announcements and credibility statements.
Why this matters for AI SEO
Owned announcements can help AI systems understand notable milestones and keep brand facts straight over time. Without that, important brand context may be missing from what AI can reference.
Next step
Create a clearly identifiable area for brand announcements so key updates are easy to find and cite.
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
❌ Sections are too short for reliable reuse
What we saw
The page is broken into many short sections, with an average section length of about 65 words, and several sections are mostly image grids with very little supporting text. That makes the content harder to parse into meaningful, self-contained chunks.
Why this matters for AI SEO
AI systems do better when they can extract complete ideas in a single pass. When sections are thin or mostly visual, the content is less likely to be summarized accurately or quoted as a useful reference.
Next step
Rewrite or consolidate the thin sections so each major section includes enough explanatory text to stand on its own.
❌ No table-based information was found
What we saw
No HTML table was detected on the page. That means there wasn’t a structured, scannable block where key details are summarized in a consistent format.
Why this matters for AI SEO
Tables can make it easier for AI to extract specifics and compare attributes without guessing. When everything is narrative or broken into small blurbs, important details can be missed or paraphrased inconsistently.
Next step
Add a simple table where it naturally fits (for example, a quick comparison or a checklist-style summary of key details).
❌ Subheadings don’t provide enough topic clarity
What we saw
Many of the subheadings were very brief or generic (examples called out included labels like “Meet Bartley” and “REMAX Classic.”). Fewer than half of the main subheadings clearly reflected the content in the sections beneath them.
Why this matters for AI SEO
Subheadings act like signposts for AI summarization and retrieval. If headings don’t clearly describe the section’s topic, it’s harder for AI to map the page and pull the right part into an answer.
Next step
Update subheadings so they clearly describe what the section answers or explains, using specific, topic-driven wording.
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.
