Overview:
On 07/14/26 deseretsingles.com scored 58% — **Fair** – Overall, the site has a solid base for AI visibility, but a few important trust and content signals aren’t coming through clearly yet.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - The site has a very solid technical foundation for discovery, though adding image or video sitemaps would help search engines better index your visual media.
- Structured Data: 58% - The homepage features a robust and error-free schema implementation including Organization and FAQ types, but the lack of resource-level markup and author data is a significant gap.
- AI Readiness: 50% - The site has a solid technical foundation with crawl-friendly robots.txt and sitemap files, though it lacks the brand identity signals like an "About" page or a Wikidata entry that LLMs look for.
- Performance: 67% - Mobile performance for the homepage is generally solid, with zero input delay and all key metrics landing safely outside of the poor range.
- Reputation: 58% - The brand is recognized by multiple LLMs and has some solid review signals, but the presence of negative client assertions and a lack of verified identity markers like a physical address are holding back its reputation score.
- LLM-Ready Content: 36% - The site is visually modern and up-to-date, but it relies on short, landing-page style copy that lacks the paragraph depth and explicit definitions AI systems prefer for detailed indexing.
The main gaps holding things back
The big picture is that the site shows strong fundamentals, but some of the deeper signals that help AI trust and clearly summarize a brand aren’t consistently showing up. Most of the gaps are more about clarity and verification than anything “wrong,” especially around identity, reputation, and how the resource content reads to a model. The next section breaks down the specific areas where those signals were missing so you can see exactly what got flagged. None of this is unusual—these are common gaps for sites that are otherwise in good shape.
Detailed Report
Discoverability
❌ Image or video sitemap not found
What we saw
We didn’t see any dedicated support for helping search systems find and understand your visual media. In the data provided, there was no sign of an image-focused or video-focused discovery layer.
Why this matters for AI SEO
Generative engines often pull visual results when they can confidently discover and interpret media content. When those signals are missing, your images and videos can be easier to overlook or misinterpret.
Next step
Add a dedicated way for crawlers to discover your image and/or video assets so they’re easier to find and index.
Structured Data
❌ Resource/blog page structured data couldn’t be evaluated
What we saw
A resource or blog page wasn’t available in the evaluation packet, so we couldn’t confirm the content-level structured signals that typically support articles and guides.
Why this matters for AI SEO
When deeper content pages aren’t sending clear, consistent signals, AI systems have less to work with when summarizing your expertise or attributing content correctly. That can reduce how often supporting content gets picked up in generative answers.
Next step
Make sure a representative resource/blog page is available for evaluation and includes clear content-level structured signals.
❌ Author on resource/blog content wasn’t identifiable
What we saw
Because the resource/blog page wasn’t provided, we couldn’t identify whether posts have a clear, non-generic author.
Why this matters for AI SEO
Author clarity helps generative engines understand who is behind the content, which affects trust and attribution. When author signals are missing or unclear, it’s harder for AI to treat the content as credible and quote-worthy.
Next step
Ensure resource/blog content clearly names a specific author (not just the brand) in a way AI systems can reliably interpret.
❌ Author profile signals (SameAs links) weren’t verifiable
What we saw
No author-related structured signals were available to review, since the resource/blog page wasn’t included in the provided data.
Why this matters for AI SEO
Without consistent identity references for authors, generative engines have a tougher time connecting content to real-world profiles and building confidence in attribution.
Next step
Add clear author identity references that connect the author to established profiles across the web.
AI Readiness
❌ About/brand context page wasn’t found
What we saw
We didn’t find internal links that clearly point to an About, Company, or Team-style page from the homepage. That means brand context wasn’t easy to confirm from the main entry point.
Why this matters for AI SEO
AI systems lean on clear brand context to understand who you are, what you do, and why you’re credible. When that context is hard to locate, it can weaken confidence in brand identity.
Next step
Create (or surface) a clear brand context page and make it easy to reach from the homepage.
❌ No Wikidata entity detected for the brand
What we saw
We didn’t detect a Wikidata entity associated with the brand in the provided data.
Why this matters for AI SEO
Generative engines often use knowledge sources to validate and disambiguate brands. When that reference point is missing, it can be harder for AI to confidently verify who the brand is.
Next step
Establish a Wikidata entity for the brand so AI systems have a stronger identity reference point.
Reputation
❌ Negative client assertions were present
What we saw
Negative client-related assertions were affirmed by at least one model in the data provided. This indicates there are trust-friction signals showing up in the broader brand narrative.
Why this matters for AI SEO
Generative engines weigh reputation signals heavily when deciding what to recommend or cite. Even a small amount of affirmed negative sentiment can reduce how confidently AI surfaces a brand.
Next step
Identify the specific negative client narratives being repeated and address them with clearer, more consistent public-facing trust signals.
❌ Brand identity appeared inconsistent
What we saw
The data indicated missing physical address details and an official-name consistency conflict. That makes the brand’s “official” identity footprint harder to confirm.
Why this matters for AI SEO
When AI systems see conflicting or incomplete identity anchors, they’re more cautious about attributing information and recommending the brand. Consistency is a big part of establishing baseline trust.
Next step
Align the brand’s core identity details so they’re consistent and complete wherever the brand is represented.
❌ No Wikidata entity was found
What we saw
No matching Wikidata entry was identified for the brand in the provided data.
Why this matters for AI SEO
A recognized entity reference can help generative engines disambiguate your brand and connect related information reliably. Without it, identity verification can be less dependable.
Next step
Create and validate a Wikidata entity that clearly represents the brand.
❌ Official identity anchors couldn’t be verified
What we saw
Because a Wikidata entity wasn’t found, the evaluation couldn’t confirm official identity anchors through that channel.
Why this matters for AI SEO
When official identity anchors can’t be cross-checked, AI systems have fewer ways to confirm legitimacy and consistency. That can reduce confidence in brand-level answers and recommendations.
Next step
Add an entity reference that includes clear official identity anchors so verification is straightforward.
❌ Homepage didn’t link to major social profiles
What we saw
No links to major social platforms were found in the homepage HTML. That removes an easy-to-verify set of official profile signals.
Why this matters for AI SEO
Generative engines look for consistent “owned” identity anchors across the web. When official profiles aren’t clearly connected, it’s harder for AI to confidently map the brand to the right entities.
Next step
Add clear homepage links to the brand’s official social profiles so identity is easier to confirm.
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
❌ Author name was generic (brand-only)
What we saw
The author was listed as “Deseret Singles,” which is the organization name rather than a specific individual. That makes it hard to tell who the content is actually coming from.
Why this matters for AI SEO
Generative engines are more confident summarizing and citing content when they can clearly attribute it to a real person or clearly defined author entity. Generic attribution can reduce trust and reduce citation likelihood.
Next step
Update the article so it credits a clear, non-generic author.
❌ Sections were too short for strong context
What we saw
The page was split into multiple sections, but the average section length was brief and read more like a landing page than a resource. That fragmentation limits how much usable context each section provides.
Why this matters for AI SEO
AI systems summarize best when each section contains enough substance to establish meaning and intent. Short, thin sections can lead to weaker or overly generic summaries.
Next step
Expand key sections so each one carries enough standalone context for an AI summary.
❌ No table was present
What we saw
No HTML table element was detected on the page. That means there wasn’t a structured “at-a-glance” block of information for quick extraction.
Why this matters for AI SEO
Structured formats can make it easier for AI to pull out key comparisons, steps, or summaries without ambiguity. When everything is paragraph-based, extraction can be less clean.
Next step
Add a simple table where it naturally fits to summarize key takeaways.
❌ Subheadings weren’t descriptive enough
What we saw
Several subheadings appeared punchy or stylistic rather than clearly describing what the following section explains. As a result, headings didn’t consistently line up with the section’s first sentences.
Why this matters for AI SEO
Descriptive headings help generative engines segment and interpret content correctly. When headings are vague, AI is more likely to miss key points or blend sections together.
Next step
Rewrite subheadings so they clearly preview the main idea of each section.
❌ Key answers didn’t show up early in most sections
What we saw
Many sections opened with very short intro lines, instead of getting quickly into the “answer” or main point. That makes the page feel lighter on immediate substance.
Why this matters for AI SEO
AI summarization tends to prioritize early, information-rich text to determine what a section is about. When the opening is thin, the model may produce less precise or less useful summaries.
Next step
Front-load each section with a fuller opening paragraph that clearly states the takeaway.
❌ Niche acronyms weren’t explained nearby
What we saw
The content used acronyms like FHE, YSA, and BYU without clear definitions close to where they appear. That can leave context gaps for readers who aren’t already familiar.
Why this matters for AI SEO
Unexplained acronyms make it harder for AI to categorize content accurately and explain it to a broad audience. That can lead to weaker topical understanding or diluted summaries.
Next step
Add quick, plain-language definitions the first time each acronym appears.
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.
