Overview:
On 02/04/26 quotivity.com scored 44% — **Below Average** – Overall, the site comes through as credible and usable, but a few missing clarity signals make it harder for AI systems to confidently understand and cite it.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - The site has a very solid technical foundation with accessible content and clear metadata, though it lacks specialized sitemaps for images and video.
- Structured Data: 0% - We weren't able to find any schema markup or a resource page, which means the site is missing the technical signals generative engines use to verify your brand and content.
- AI Readiness: 50% - The site has a strong technical start with AI-friendly crawling and sitemaps, but it's missing the brand context and Wikidata presence needed for better entity recognition.
- Performance: 50% - Mobile performance generally landed outside the 'poor' range, although the time it takes for the main content to load is a significant bottleneck.
- Reputation: 46% - The brand has a clean reputation with no negative feedback found, but it lacks the independent validation from press, reviews, and Wikidata that search engines use to establish high-level authority.
- LLM-Ready Content: 28% - This landing page uses descriptive subheadings and front-loaded answers, but lacks the authorship and date metadata needed to establish strong content authority for AI systems.
What stands out most overall
The big picture is that the site has a decent foundation, but it’s missing several of the signals AI systems use to confidently identify the brand, validate reputation, and reuse content. These aren’t “errors” as much as visibility gaps—places where the story isn’t being stated clearly enough for machines to confirm. The next sections walk through the specific areas where those gaps showed up, organized by category. None of this is unusual, and it’s all the kind of stuff that can be tightened up once you know exactly what’s getting missed.
Detailed Report
Discoverability
❌ No image or video sitemap found
What we saw
We didn’t see a dedicated image or video sitemap available for the site. That means visual assets don’t have a clear, centralized place where they’re listed for discovery.
Why this matters for AI SEO
AI-driven search experiences increasingly pull in images and videos alongside text. When visual content is harder to discover, it can reduce how often your brand shows up for visual-first or blended results.
Next step
Create and publish an image sitemap and/or video sitemap so visual content is easier to find and understand.
Structured Data
❌ No structured data found on the homepage
What we saw
We didn’t find structured data on the homepage. As a result, key details about the brand and what the site represents aren’t being stated in a machine-friendly way.
Why this matters for AI SEO
Structured data helps AI systems reliably identify entities like brands, organizations, and offerings. Without it, engines have to infer more, which can lead to weaker or less consistent visibility.
Next step
Add structured data to the homepage that clearly describes the brand and what it does.
❌ Organization-level structured data not confirmed
What we saw
Because no structured data was detected, we couldn’t confirm any organization-focused markup. That leaves basic brand identity details less explicit.
Why this matters for AI SEO
When brand identity is clearly defined, AI systems are more confident about matching your site to the right entity and describing it accurately. Missing organization signals can make that connection less reliable.
Next step
Include organization-focused structured data so the site can state its identity more clearly.
❌ Resource/blog structured data wasn’t evaluable
What we saw
A resource or blog page wasn’t provided for review, so we couldn’t check content-level structured data there. That leaves a blind spot around how article-style pages present key details.
Why this matters for AI SEO
AI systems often rely on consistent content metadata (like authorship and publishing context) when deciding what to cite. If those signals aren’t accessible or consistent, content can be harder to trust and reuse.
Next step
Provide a representative resource/blog URL for evaluation and ensure it includes clear, machine-readable content context.
❌ “No major structured data errors” couldn’t be verified
What we saw
This check couldn’t pass because no structured data was found to validate. In other words, there wasn’t anything present to confirm as clean and usable.
Why this matters for AI SEO
When structured data is present and valid, it becomes a dependable input for AI understanding. If it’s missing entirely, you lose that dependable layer of clarity.
Next step
Implement structured data and validate that it’s readable and consistent.
❌ Author information on resource/blog content wasn’t evaluable
What we saw
We couldn’t evaluate whether a resource/blog post shows a clear, non-generic author because a resource/blog page wasn’t included. That means authorship signals weren’t available to confirm.
Why this matters for AI SEO
Clear authorship helps AI systems assess credibility and attribute information correctly. Without confirmable author signals, content can come across as less authoritative.
Next step
Ensure key content pages visibly include a specific author and make them available for review.
❌ Author profile links (sameAs) weren’t evaluable
What we saw
Because the resource/blog page and author data weren’t available, we couldn’t confirm whether author profiles connect to consistent identity links across the web.
Why this matters for AI SEO
Connected identity signals help AI systems distinguish real people and reduce ambiguity. When those connections aren’t present or can’t be verified, it’s easier for author identity to get diluted.
Next step
Use consistent author identity references on content pages so authors are easier for AI systems to recognize.
AI Readiness
❌ No clear “About/Company” path found from the homepage
What we saw
We didn’t detect an internal link from the homepage that clearly points to an About/Company/Team-style page. That makes it harder to quickly confirm who’s behind the brand.
Why this matters for AI SEO
AI systems look for straightforward brand background cues when summarizing or recommending companies. When that context isn’t easy to find, it can reduce confidence and lead to vaguer descriptions.
Next step
Make sure there’s a clearly labeled path from the homepage to a dedicated brand context page.
❌ No Wikidata entity found for the brand
What we saw
We didn’t find a Wikidata entry associated with this brand/domain. That leaves the brand without a widely used public entity reference point.
Why this matters for AI SEO
Entity databases can help AI systems resolve “who is this?” questions more confidently. When that anchor is missing, brand recognition and consistency can be harder to maintain across models.
Next step
Create or claim a Wikidata entity for the brand with consistent identity details.
Performance
❌ Slow initial load on the homepage
What we saw
The homepage’s primary content took a long time to appear in the initial load experience. This makes the first impression feel sluggish.
Why this matters for AI SEO
If a page is slow to fully show key content, it can reduce how reliably systems and users engage with it. Over time, that can limit how often the page gets treated as a strong candidate for visibility.
Next step
Reduce the time it takes for the homepage’s main content to load and become visible.
Reputation
❌ Identity details aren’t consistently confirmed offsite
What we saw
While the brand name and domain are consistent, we didn’t see a strong consensus around key identity details like a physical business address. That makes the brand footprint feel a bit harder to verify.
Why this matters for AI SEO
AI systems are more confident when multiple sources corroborate the same identity facts. When those details are inconsistent or missing, trust and entity matching can be weaker.
Next step
Align and publish consistent brand identity details across the main places third parties reference.
❌ No Wikidata match for the brand
What we saw
We didn’t find a Wikidata entity associated with the brand. That removes a common reference point used in entity-based understanding.
Why this matters for AI SEO
When a brand has a clear entity record, AI systems can connect facts more consistently. Without it, recognition can be more uneven across different models and surfaces.
Next step
Establish a Wikidata entry that matches the brand and reflects consistent identity information.
❌ Wikidata identity anchors aren’t present
What we saw
Because no Wikidata entity was found, there were no related identity “anchor” references to corroborate the brand. This leaves fewer dependable offsite signals for automated systems.
Why this matters for AI SEO
AI systems tend to rely on repeatable, well-linked sources when forming confident summaries. Missing anchors can make brand details easier to miss or misinterpret.
Next step
Add a Wikidata entity and ensure it connects to the brand’s canonical web profiles.
❌ Third-party reviews weren’t clearly verifiable
What we saw
We didn’t see a consistent signal that third-party reviews exist in a way models could agree on. That makes external validation of customer feedback feel thin.
Why this matters for AI SEO
Independent feedback is one of the easiest trust shortcuts for AI summaries and comparisons. When it’s not clearly verifiable, the brand can come across as harder to evaluate.
Next step
Build a clearer trail of third-party review presence that’s easy to confirm.
❌ Review sources and counts weren’t concrete
What we saw
There wasn’t strong agreement on specific review sources or concrete review counts. That leaves reputation signals less grounded in externally confirmable references.
Why this matters for AI SEO
AI systems tend to favor reputation details that can be tied to recognizable sources. When sources aren’t concrete, reputation gets summarized more cautiously—or not at all.
Next step
Make sure review sources are clearly attributable and consistently referenced.
❌ Social profile recognition wasn’t consistent
What we saw
Even though social links exist on the site, the models didn’t show consistent consensus around the brand’s social profiles. That suggests the offsite footprint isn’t translating into a strong, consistent identity signal.
Why this matters for AI SEO
When profiles are consistently recognized, they help validate the brand and connect entity details across the web. Inconsistency can lead to weaker attribution and less stable brand summaries.
Next step
Strengthen the consistency of brand identity across the primary social profiles that represent the company.
❌ Independent press coverage wasn’t confirmed
What we saw
We didn’t find a clear consensus that the brand has meaningful independent press mentions. That makes the brand’s third-party visibility feel limited.
Why this matters for AI SEO
Independent mentions act as credibility signals that AI systems can reference when explaining who you are. Without them, the brand can appear “clean but quiet,” which limits confidence.
Next step
Increase the brand’s presence in independently published sources that are easy to verify.
❌ Owned press coverage wasn’t confirmed
What we saw
We didn’t see a clear signal of owned media coverage (like publicly accessible announcements) that models could agree on. That reduces the amount of “official” narrative material available.
Why this matters for AI SEO
Owned announcements can provide reliable, quotable context about milestones and positioning. When that footprint is thin, AI systems have fewer dependable references to pull from.
Next step
Publish and maintain a clear, accessible stream of official announcements that reinforce brand narrative.
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 clear author listed
What we saw
We didn’t see a visible author name tied to the article. As a reader (and as an AI system), it’s not clear who is responsible for the content.
Why this matters for AI SEO
Authorship is a major trust cue for AI summaries and citations. When it’s missing, the content can be treated as less attributable and less authoritative.
Next step
Add a specific, non-generic author name to the article.
❌ No publish or update date
What we saw
We couldn’t find a publication date or last-updated date displayed for the article. That makes freshness and context harder to judge.
Why this matters for AI SEO
AI systems often weigh whether information is current, especially for product/process content. Without a date, it’s harder for them to confidently reuse or recommend it.
Next step
Show a clear publish date or last updated date on the page.
❌ Recency couldn’t be confirmed
What we saw
Because no date was present, we couldn’t confirm whether the content has been updated recently. The page doesn’t give a clear freshness signal.
Why this matters for AI SEO
Freshness can influence whether AI systems consider a page safe to cite for “current best practice” queries. Unclear recency can reduce how often it’s pulled into answers.
Next step
Add an update date when content is refreshed so recency is explicit.
❌ No external (non-social) outbound links
What we saw
All links detected were internal to the brand’s own sites and subdomains, with no external references. That limits the amount of third-party support for any claims or definitions.
Why this matters for AI SEO
External references can strengthen trust and help AI systems validate terminology and assertions. When everything stays internal, the content can feel less corroborated.
Next step
Include at least one relevant external, non-social reference link where it genuinely supports the content.
❌ Sections are too fragmented for deep extraction
What we saw
While the page uses a lot of subheadings, the sections themselves are very short and don’t develop a complete thought. That makes the article feel more like a set of snippets than a few strong, self-contained blocks.
Why this matters for AI SEO
LLMs tend to extract and reuse content more reliably when sections contain enough context to stand on their own. Over-fragmented sections can lead to shallow or incomplete excerpts.
Next step
Rewrite sections so each one contains enough context and detail to be clearly understood in isolation.
❌ No table-based summary content
What we saw
We didn’t find any table element used to summarize or compare information. That removes a common “quick extraction” format.
Why this matters for AI SEO
Tables can make definitions, comparisons, and steps easier for AI systems to parse accurately. Without them, some structured takeaways can be harder to extract cleanly.
Next step
Add a simple table where it naturally helps summarize key comparisons or steps.
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.
