Overview:
On 04/07/26 erikamiguel.com scored 53% — **Fair** – Overall, the site has a solid base, but a few missing and hard-to-verify signals are keeping it from feeling fully “complete” for AI visibility.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - This section is looking really good, though we noticed the site is missing dedicated sitemaps for images and video.
- Structured Data: 58% - The homepage is well-optimized with organization and personal schema, though we couldn't confirm author or blog-specific markup without a resource page.
- AI Readiness: 50% - The site is wide open for AI crawlers and provides clear brand context, though it's missing update timestamps in the sitemap and a verified Wikidata identity.
- Performance: 67% - Mobile performance is looking solid across the board, with all homepage metrics staying well clear of the thresholds that search engines consider poor.
- Reputation: 12% - Most of the reputation signals couldn't be verified due to missing data, though the site does correctly link to its social profiles.
- LLM-Ready Content: 64% - The site is well-structured and author-verified with recent updates, though its headings and introductory paragraphs are too brief to fully optimize for AI content extraction.
The main takeaway before details
The big picture is that the core site foundation reads clearly, but several supporting signals are either missing or couldn’t be verified from the available inputs. The result isn’t “bad” so much as a few visibility and confidence gaps that make it harder for AI systems to fully understand and trust what they’re seeing. The next section walks through the specific areas where the evaluation came up short, organized by category. With a clearer set of signals in those spots, the overall story becomes much easier for AI to pick up and repeat accurately.
Detailed Report
Discoverability
❌ Image or Video Sitemap Exists
What we saw
We didn’t detect an image sitemap or a video sitemap for the site. That means visual content doesn’t have an explicit discovery path in the evaluation results.
Why this matters for AI SEO
Generative engines often pull in visual assets when they’re trying to understand what a brand does and what it has produced. If visual content is harder to discover, it’s less likely to be surfaced or referenced.
Next step
Add an image and/or video sitemap (where relevant) so visual assets have a clearer path to being discovered and indexed.
Structured Data
❌ Schema Markup Present on Resource / Blog Page
What we saw
The resource/blog page content needed for evaluation was missing or empty, so we couldn’t verify structured data on a representative article or resource page.
Why this matters for AI SEO
When AI systems summarize or cite content, they lean on clear page-level context to understand what the page is and how it should be interpreted. If that context can’t be confirmed, the page is harder to confidently classify and reuse.
Next step
Provide a specific resource/blog URL for review and ensure that page includes clear structured data that describes the content.
❌ Resource / Blog Post Has a Clear, Non-Generic Author
What we saw
Because the resource/blog page content was missing or empty, we couldn’t confirm that an individual article/resource clearly names a non-generic author.
Why this matters for AI SEO
Authorship is a major trust and attribution cue for generative engines. Without a clear author signal on content pages, it’s harder for AI to treat the content as credible and attributable.
Next step
Ensure each resource/blog post clearly identifies a specific author on the page.
❌ Author Schema Includes SameAs Links
What we saw
The resource/blog page content was missing or empty, so we couldn’t verify any author “sameAs” profile links tied to content authors.
Why this matters for AI SEO
Profile links help AI systems connect an author to consistent, recognizable identities across the web. When those connections aren’t present (or can’t be verified), author credibility is harder to establish.
Next step
Add author profile links that consistently point to the author’s canonical public profiles.
AI Readiness
❌ XML Sitemap Contains Lastmod Data
What we saw
The XML sitemap was present, but it did not include last modified timestamps. That makes it unclear when specific pages were last updated.
Why this matters for AI SEO
Freshness context helps AI systems decide what’s current and which pages to prioritize when generating answers. Without clear update signals, newer or recently refreshed content may be harder to recognize.
Next step
Include last modified dates in the sitemap entries so update timing is explicit.
❌ Wikidata Entity Exists for Brand
What we saw
No Wikidata item ID was found for the brand in the evaluation results. As a result, there isn’t a verified entity reference available to cross-check identity.
Why this matters for AI SEO
Entity anchors help generative engines consistently understand “who” a brand is, especially when names overlap or context is limited. Without that anchor, identity can be more ambiguous across AI surfaces.
Next step
Create or claim a Wikidata entity for the brand and connect it to official identity references.
Reputation
❌ No Affirmed Negative Client Assertions
What we saw
This couldn’t be verified because the required field was missing from the provided data packet. As a result, we don’t have enough information here to confirm what client sentiment looks like.
Why this matters for AI SEO
AI systems weigh trust signals when deciding whether to recommend or cite a brand. If reputation context can’t be verified, confidence and visibility can be harder to earn.
Next step
Provide the missing client reputation data so this can be validated reliably.
❌ No Affirmed Negative Employee Assertions
What we saw
This couldn’t be verified because the required field was missing from the provided data packet. We weren’t able to confirm what employee sentiment looks like from the available inputs.
Why this matters for AI SEO
Reputation signals help AI systems decide how safe it is to surface a brand in recommendations. Missing verification points can reduce how confidently AI systems represent the business.
Next step
Provide the missing employee reputation data so this can be reviewed accurately.
❌ Brand Recognized by Multiple LLMs
What we saw
This couldn’t be confirmed because the required recognition field was missing from the provided data packet. That means we can’t verify how consistently the brand is recognized across AI systems.
Why this matters for AI SEO
Consistency of recognition is a proxy for how “known” and unambiguous a brand is in AI contexts. If recognition can’t be verified, it’s harder to gauge how reliably the brand shows up in answers.
Next step
Provide the missing recognition data needed to verify cross-model brand recognition.
❌ Brand Identity Consistent
What we saw
This couldn’t be validated because the identity consensus fields were missing from the provided data packet. We couldn’t confirm whether key identity details resolve consistently.
Why this matters for AI SEO
When brand identity details are consistent, AI systems are more likely to describe the business accurately and avoid confusion. If that consistency can’t be verified, the brand can be easier to misinterpret.
Next step
Provide the missing identity consensus data so brand consistency can be assessed.
❌ Wikidata Entity Exists and Matches Brand
What we saw
This check failed because the required matching-status field was missing from the provided data packet. We couldn’t confirm whether a Wikidata entity exists and aligns with the brand.
Why this matters for AI SEO
A matched entity helps AI systems anchor identity and reduce ambiguity. Without verification, it’s harder for AI to confidently connect the brand to a single, authoritative reference.
Next step
Provide the missing Wikidata match information so this can be verified.
❌ Wikidata Has Official Identity Anchors
What we saw
This couldn’t be confirmed because the required “official website” field was missing from the provided data packet. We weren’t able to verify whether official identity anchors exist.
Why this matters for AI SEO
Official anchors help AI systems tie a brand entity back to the right source of truth. If those anchors can’t be validated, identity resolution becomes less reliable.
Next step
Provide the missing identity anchor data so the brand’s official references can be confirmed.
❌ Third-Party Reviews or Customer Feedback Exists
What we saw
This couldn’t be verified because the required reviews field was missing from the provided data packet. We weren’t able to confirm the presence of third-party feedback from the available inputs.
Why this matters for AI SEO
Third-party feedback is one of the clearest trust signals AI systems can reference. If review presence can’t be verified, it may limit how confidently a brand can be recommended.
Next step
Provide the missing review/feedback data so third-party trust signals can be confirmed.
❌ Review Sources Are Concrete
What we saw
This check failed because the required review source count field was missing from the provided data packet. We couldn’t validate whether reviews are tied to concrete, attributable sources.
Why this matters for AI SEO
Generative engines prefer to reference information they can attribute to recognizable sources. If sources aren’t verifiable, review signals are less useful for building confidence.
Next step
Provide the missing review source details so attribution can be confirmed.
❌ LLM Consensus on Major Social Profiles
What we saw
This couldn’t be verified because the required consensus field was missing from the provided data packet. We weren’t able to confirm whether major social profiles resolve consistently.
Why this matters for AI SEO
Consistent profile associations reduce confusion about which accounts are official. Without that verification, AI systems may be less confident about referencing or recommending the right profiles.
Next step
Provide the missing social profile consensus data so official accounts can be validated.
❌ Independent (Offsite) Press or Coverage Exists
What we saw
This check failed because the required independent press field was missing from the provided data packet. We couldn’t confirm whether there are independent mentions or coverage.
Why this matters for AI SEO
Independent coverage can act as an external credibility signal that AI systems may rely on. If this can’t be verified, the brand may look less established in broader contexts.
Next step
Provide the missing offsite press/coverage data so independent credibility signals can be reviewed.
❌ Owned / ONSITE Press or Press Releases Exist
What we saw
This couldn’t be verified because the required owned press field was missing from the provided data packet. We weren’t able to confirm whether there are onsite press mentions or releases.
Why this matters for AI SEO
Press-style pages can help AI systems quickly understand notable updates, launches, or proof points tied to a brand. If those signals can’t be confirmed, the brand story may appear thinner than it is.
Next step
Provide the missing onsite press/press release data so it can be validated.
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
❌ HTML Table Present
What we saw
We didn’t see an HTML table in the content. That means there wasn’t a clearly structured comparison or quick-reference block for systems to parse.
Why this matters for AI SEO
AI systems often extract structured facts more reliably when information is presented in a tight, consistent format. Without that structure, key details can be harder to pull out cleanly.
Next step
Add a simple table where it genuinely fits the content (for example, a comparison, a services breakdown, or a quick FAQ summary).
❌ Descriptive Subheadings
What we saw
The subheadings were generic and short (for example, labels like “Services,” “Portfolio,” and “FAQ”). As written, they don’t add much topic detail beyond broad section names.
Why this matters for AI SEO
Subheadings act like signposts for what each section is actually about. If they’re too generic, AI systems have less context for indexing and summarizing the specific points inside each section.
Next step
Rewrite subheadings so they describe the specific questions, offers, or topics covered in each section.
❌ Key Answers Appear Early
What we saw
The opening paragraphs across sections were very brief, and none met the depth threshold used in the evaluation. As a result, key context doesn’t show up early in each section.
Why this matters for AI SEO
Generative engines often rely on the first bit of a section to quickly determine what it covers and what it should extract. If the early context is thin, the model may miss or oversimplify what you want it to understand.
Next step
Expand each section’s opening so the main point and supporting context are clear right away.
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.
