Overview:
On 01/29/26 heritage.org scored 52% — **Fair** – Overall, the site looks reasonably visible, but a few missing clarity signals make it harder for AI systems to confidently interpret the brand and content.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - The site has a very solid technical foundation for discoverability, though adding specialized image and video sitemaps would help ensure its media content is fully indexed.
- Structured Data: 0% - We weren't able to find any schema markup on the homepage or a resource page, which is a significant missed opportunity for establishing site authority.
- AI Readiness: 67% - The site is technically very accessible for AI crawlers and has solid sitemap data, though it lacks a formal Wikidata entry for brand verification.
- Performance: 50% - Mobile performance is generally stable and responsive, but the initial page load speed on the homepage is a major bottleneck.
- Reputation: 65% - The brand has excellent recognition and great social signals, but we did find some negative employee feedback and a missing Wikidata link.
- LLM-Ready Content: 32% - The site identifies expert authors and maintains high readability, but it lacks the descriptive subheadings and detailed section content that help AI systems extract and reuse information.
What stands out most overall
The main takeaway is that the site has a decent baseline for being discovered, but it’s missing several signals that help AI systems confidently interpret identity, credibility, and content context. These gaps are less about “bad content” and more about clarity—what the brand is, who’s behind the content, and how current and well-framed each section is. Next, the report breaks down the specific areas where those signals didn’t show up so you can see exactly what’s getting in the way. None of this is unusual, and it’s all the kind of stuff that becomes straightforward once it’s clearly identified.
Detailed Report
Discoverability
❌ No image or video sitemap found
What we saw
We didn’t see any dedicated image or video sitemap called out 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
Generative engines often lean on well-organized signals to understand what media exists and how it relates to the site’s topics. When that visibility is limited, your images and videos are less likely to be confidently surfaced and referenced.
Next step
Create and publish an image and/or video sitemap that lists key visual assets you want discovered.
Structured Data
❌ No structured data found on the homepage
What we saw
We didn’t detect any valid structured data on the homepage. In other words, there wasn’t a clear machine-readable layer describing what the organization is.
Why this matters for AI SEO
When structured data isn’t present, AI systems have a harder time verifying key facts and relationships about the brand. That can reduce confidence when summarizing or recommending the site.
Next step
Add structured data to the homepage so the organization and core site identity are explicitly described.
❌ No organization-type structured data on the homepage
What we saw
We didn’t see an organization-related structured data type on the homepage. That leaves the brand’s identity and entity details less explicit to machines.
Why this matters for AI SEO
Organization-level structured data helps generative engines connect the site to a consistent “who” behind the content. Without it, identity signals can be weaker or more ambiguous.
Next step
Include an organization-type structured data block that clearly represents the brand.
❌ Resource/blog structured data couldn’t be evaluated
What we saw
A resource or blog page wasn’t provided for this part of the evaluation, so we couldn’t confirm whether structured data is used on content pages. As a result, this area was treated as missing.
Why this matters for AI SEO
Content pages are often where AI systems look for clear signals about what a piece is, who wrote it, and how to attribute it. If those signals can’t be confirmed, confidence in citation and categorization tends to drop.
Next step
Ensure resource/blog pages include structured data that identifies the content type and key details.
❌ Structured data quality couldn’t be confirmed
What we saw
Because no structured data was detected, there wasn’t anything to assess for major structured data issues. Under the evaluation rules, that results in this being marked as a failure.
Why this matters for AI SEO
AI systems benefit from structured data that’s both present and clean, since messy or missing signals can limit trust and consistency. If there’s no structured data to rely on, engines fall back to inference.
Next step
Once structured data is added, review it to confirm it’s complete and free of major formatting issues.
❌ Author identity couldn’t be verified on a resource/blog post
What we saw
Without a resource/blog page to review, we couldn’t verify whether posts have a clear, non-generic author shown. This was marked as missing due to the unavailable page.
Why this matters for AI SEO
Authorship is a big part of how AI systems evaluate credibility and attribution. When author identity isn’t clearly verifiable, it can make content harder to trust and cite.
Next step
Make sure resource/blog posts consistently display an identifiable author.
❌ Author profile connections (sameAs) not detected
What we saw
No author structured data was available to confirm “sameAs” links to verified external profiles. This was flagged because the needed page context wasn’t available.
Why this matters for AI SEO
When author entities connect cleanly to known profiles, AI systems can more easily disambiguate who the author is and consolidate authority signals. Without those connections, author trust can be fuzzier.
Next step
Add author structured data that includes sameAs links to official, relevant author profiles.
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 provided data. That means there isn’t a confirmed public entity record for AI systems to reference.
Why this matters for AI SEO
Wikidata is one of the clearest third-party “anchors” used to validate brand identity across systems. When it’s missing, AI engines may have a harder time confidently connecting the site to the right entity.
Next step
Create or claim a Wikidata entity for the brand and associate it with official brand identifiers.
Performance
❌ Slow initial visual load on the homepage
What we saw
The homepage’s main content took a very long time to appear during testing. Even though the page may feel stable once it’s up, that first visual load is a clear bottleneck.
Why this matters for AI SEO
When a page is slow to fully present its primary content, it can reduce how reliably systems can access and interpret what the page is about. Over time, that can limit consistent discovery and summarization.
Next step
Reduce the time it takes for the homepage’s primary content to appear.
Reputation
❌ Negative client-side assertions were found
What we saw
We saw negative client-side assertions referenced in the research, including criticisms related to specific studies. This introduces some conflicting third-party sentiment into the brand’s footprint.
Why this matters for AI SEO
Generative engines don’t just look at what you say about yourself—they also weigh what others say. When negative assertions are prominent, systems may hedge or qualify how they describe the brand.
Next step
Review the most common external criticisms showing up and ensure your public narrative addresses them with clear, verifiable context.
❌ Negative employee-side assertions were found
What we saw
We saw negative employee-side assertions in the research, including feedback related to the work environment. This can create mixed trust signals around the organization.
Why this matters for AI SEO
AI systems often blend signals from multiple independent sources to form an overall reputation read. If employee sentiment skews negative in visible sources, it can affect how confidently the brand is framed.
Next step
Audit the main third-party sources where employee sentiment appears and make sure the brand story is consistent and well-supported elsewhere.
❌ No confirmed Wikidata match for the brand
What we saw
We weren’t able to confirm a Wikidata match for the brand. This leaves a gap in widely recognized entity verification.
Why this matters for AI SEO
Wikidata functions like a common reference point across many knowledge systems. Without a match, it’s harder for AI to reliably connect identity details across the web.
Next step
Establish a verified Wikidata entity for the brand so identity signals have a consistent public reference.
❌ Wikidata identity anchors were not found
What we saw
No verified identifiers or official website anchors were found via Wikidata. That means key “proof points” weren’t present in that ecosystem.
Why this matters for AI SEO
Identity anchors help AI systems resolve confusion and confirm they’re referencing the right entity. When those anchors are missing, attribution and consistency can suffer.
Next step
Add the brand’s official website and relevant identifiers as anchors within Wikidata.
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 visible publish or update date
What we saw
We didn’t find a visible publication date or update date for the content sections reviewed. There also wasn’t a date signal available in the evaluation output.
Why this matters for AI SEO
Dates help AI systems judge freshness and contextual relevance, especially for topics that can change over time. When dates aren’t clear, engines may be more cautious about using the content.
Next step
Add a clear publish date (and update date when relevant) where users and AI systems can easily find it.
❌ Recency couldn’t be confirmed
What we saw
Because no publication or modification date was detected, the content’s recency couldn’t be confirmed. This was flagged even if the content might actually be current.
Why this matters for AI SEO
Generative engines often prioritize sources that clearly signal they’re current. If recency can’t be established, content may be treated as less reliable for up-to-date answers.
Next step
Make recency unambiguous by ensuring the content includes a detectable publish or last-updated date.
❌ Sections are too thin to stand on their own
What we saw
While the page is broken into sections, the sections were described as significantly shorter than the expected amount of text, making the content feel fragmentary. That can make it harder to understand each block in isolation.
Why this matters for AI SEO
AI systems tend to extract meaning at the section level, not just the page level. If sections don’t contain enough self-contained context, it’s easier for key ideas to get lost or misclassified.
Next step
Expand key sections so each one contains enough standalone context to be clearly understood.
❌ No table-based summary content detected
What we saw
No HTML table element was detected on the page. That means there wasn’t a simple structured summary block for quick scanning.
Why this matters for AI SEO
Tables can make key facts and comparisons easier for systems to extract accurately. Without them, important details may be spread across the page in less structured ways.
Next step
Add a simple table where it naturally fits to summarize key facts, comparisons, or takeaways.
❌ Subheadings are too generic
What we saw
Many subheadings were described as short, generic labels (for example, “Video,” “Events,” and “Awards”) that don’t provide much topical context. This makes it harder to understand what each section is actually about.
Why this matters for AI SEO
Subheadings act like signposts for AI systems to categorize and summarize content quickly. If they’re vague, engines may misinterpret sections or overlook them.
Next step
Rewrite generic subheadings so they clearly describe what the reader will learn in that section.
❌ Key context doesn’t show up early in sections
What we saw
The evaluation indicated that many sections don’t begin with a substantial opening paragraph that quickly explains the point. As a result, the immediate “why this section matters” context isn’t consistently present.
Why this matters for AI SEO
Generative engines often rely on early section text to decide what the section represents and whether it’s useful to cite. If that context arrives late (or not at all), the section can be undervalued.
Next step
Ensure each major section starts with a short, clear opener that states the main point upfront.
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.
