Overview:
On 06/17/26 tess.global/ scored 46% — **Below Average** – Overall, the site has some solid groundwork, but a few missing signals make it harder for AI systems to confidently understand and reference the brand and its content.
Executive summary
Score Breakdown (High Level)
- Discoverability: 100% - The site is fully accessible to search engines and has all its core metadata in place, though it lacks specialized sitemaps for images and video.
- Structured Data: 58% - The homepage is off to a good start with clean organizational schema, but we couldn't confirm if the blog content is backed by the same level of structured data or clear authorship.
- AI Readiness: 50% - The site has the core basics like a sitemap and an accessible robots.txt file, but it's missing some technical markers like sitemap timestamps and a Wikidata profile.
- Performance: 67% - Mobile performance is in good shape across the board, with the homepage showing solid stability and quick response times.
- Reputation: 12% - The site looks solid with its social media integration on the homepage, but we weren't able to confirm broader offsite signals like press coverage or a verified Wikidata entry.
- LLM-Ready Content: 32% - The page identifies its founders and professional credentials clearly, but it lacks the editorial structure—like specific dates and deep, descriptive subheadings—that helps AI systems verify content depth and currency.
What stands out most overall
The big picture is that the site is generally easy to access and understand at a baseline, but it’s missing several signals that help AI systems verify the brand and confidently reuse the content. Most of what’s showing up here isn’t “wrong,” it’s more that key context and trust cues aren’t clearly established or couldn’t be confirmed from the available information. The next sections break down the specific areas where those gaps showed up, from brand reputation signals to content formatting and freshness cues. Once you see them laid out, it should feel pretty straightforward to prioritize what matters most.
Detailed Report
Discoverability
❌ No image or video sitemap detected
What we saw
We didn’t find any dedicated image or video sitemap. Everything else in discovery looked present, but media-specific discovery support wasn’t surfaced.
Why this matters for AI SEO
AI and search systems rely on clear source signals to find and understand key assets. When media isn’t clearly mapped, it can be easier for important visuals or videos to get overlooked or under-attributed.
Next step
Publish and reference dedicated image and/or video sitemaps so media assets are easier to discover and interpret.
Structured Data
❌ Blog/resource schema could not be evaluated
What we saw
We weren’t able to find or review structured data for the resource/blog page because the page data wasn’t available in the packet.
Why this matters for AI SEO
When content pages don’t have clear, machine-readable context, AI systems have a harder time confidently categorizing what the page is and when it should be cited.
Next step
Ensure your resource/blog pages provide structured data that clearly describes the content type and page details.
❌ Blog/resource author couldn’t be verified
What we saw
Authorship for the evaluated resource/blog post couldn’t be confirmed because the resource page data wasn’t provided.
Why this matters for AI SEO
Clear authorship is a major trust cue for AI summaries and citations, especially for “who said this?” style questions. Without it, the content can feel less attributable.
Next step
Make sure each resource/blog post clearly identifies a specific author in a consistent, non-generic way.
❌ Author identity links weren’t found (sameAs)
What we saw
We couldn’t evaluate any author profile markup or identity links because author schema wasn’t available from the resource/blog page data.
Why this matters for AI SEO
Identity links help AI systems connect an author to their broader, real-world presence. Without those connections, it’s harder for models to be confident about “who the author is” across sources.
Next step
Add consistent author identity links that connect the author to their official profiles.
AI Readiness
❌ Sitemap freshness information missing
What we saw
The XML sitemap was found, but it didn’t include last-updated information to indicate which pages are newest or recently refreshed.
Why this matters for AI SEO
Freshness cues help crawlers and AI systems prioritize what to re-check and what to treat as current. When that signal isn’t clear, newer updates can take longer to be recognized.
Next step
Include last-updated signals in the sitemap so content changes are easier to interpret.
❌ No brand Wikidata entity found
What we saw
We didn’t find a Wikidata item ID associated with the brand.
Why this matters for AI SEO
Wikidata is one of the common reference points AI systems use to confirm entity identity details. When it’s missing, verification and “about this brand” understanding can be less consistent.
Next step
Create or claim a Wikidata entity for the brand and align it with the brand’s official identity details.
Reputation
❌ Couldn’t confirm absence of negative client assertions
What we saw
We weren’t able to verify whether there are affirmed negative client claims because the needed offsite reputation data wasn’t available in the provided packet.
Why this matters for AI SEO
When AI systems summarize a brand, unresolved sentiment signals can make the brand profile less stable or less confidently framed.
Next step
Compile and validate client sentiment signals so brand summaries can be grounded in clear, confirmable sources.
❌ Couldn’t confirm absence of negative employee assertions
What we saw
We couldn’t confirm whether there are affirmed negative employee claims because that reputation data wasn’t included in the packet.
Why this matters for AI SEO
Employee sentiment is part of the broader trust picture that can influence how confidently AI systems describe the brand.
Next step
Gather and verify employee-related reputation signals so AI-generated brand descriptions have a clearer trust baseline.
❌ Brand recognition across models couldn’t be confirmed
What we saw
We weren’t able to confirm whether multiple AI systems consistently recognize the brand because recognition data wasn’t provided.
Why this matters for AI SEO
Inconsistent recognition makes it harder for AI engines to confidently return the right brand entity, especially when names are similar across industries.
Next step
Validate that the brand is consistently recognized and referenced using the same identity details across major AI surfaces.
❌ Brand identity consistency couldn’t be validated
What we saw
We couldn’t confirm whether the brand’s identity details resolve consistently because the identity consensus data wasn’t available.
Why this matters for AI SEO
When identity details aren’t consistent across sources, AI systems may hedge on descriptions or mix attributes with similarly named entities.
Next step
Document and confirm consistent brand identity details across the places AI systems commonly pull from.
❌ Wikidata match status couldn’t be confirmed
What we saw
We couldn’t verify a Wikidata match for the brand because matching status data wasn’t included in the packet.
Why this matters for AI SEO
A confirmed match helps AI systems anchor the brand to a single, stable entity record instead of treating it as ambiguous.
Next step
Confirm whether a matching Wikidata entity exists and aligns with the brand’s official identity.
❌ Official identity anchors on Wikidata couldn’t be verified
What we saw
We weren’t able to confirm whether Wikidata includes official identity anchors (like an official website) because that data wasn’t available.
Why this matters for AI SEO
Official anchors reduce confusion and make it easier for AI systems to tie brand mentions back to the correct source.
Next step
Ensure the brand’s entity record includes official identity anchors that point back to the brand.
❌ Third-party reviews couldn’t be confirmed
What we saw
We weren’t able to confirm whether third-party reviews or customer feedback exist because review data wasn’t provided.
Why this matters for AI SEO
Reviews are a common trust input for AI summaries, especially when users ask for “best” or “most trusted” options.
Next step
Compile and confirm third-party review signals so trust is easier for AI systems to reference.
❌ Review sources weren’t concrete or verifiable
What we saw
We couldn’t validate specific, concrete review sources because the source data wasn’t included in the packet.
Why this matters for AI SEO
AI systems are more likely to reuse reputation details when they can be tied to clearly named, third-party sources.
Next step
Make sure review sources are clearly documented and attributable to specific third-party platforms.
❌ Consensus on official social profiles couldn’t be confirmed
What we saw
Even though social links were detected on the homepage, we couldn’t confirm broader consensus on the brand’s official profiles because the supporting consensus data wasn’t provided.
Why this matters for AI SEO
When official profiles are unambiguous, AI systems can more confidently verify the brand and reference the right accounts.
Next step
Confirm and align the brand’s official social profiles so they resolve consistently across sources.
❌ Independent press or coverage couldn’t be confirmed
What we saw
We weren’t able to confirm whether independent, offsite press mentions exist because press data wasn’t included.
Why this matters for AI SEO
Independent coverage helps AI systems distinguish between self-published claims and third-party validation.
Next step
Collect and confirm independent coverage sources so third-party validation is easier to reference.
❌ Owned press or press releases couldn’t be confirmed
What we saw
We couldn’t confirm whether the site publishes press releases or onsite press mentions because that data wasn’t available.
Why this matters for AI SEO
Clear brand announcements and updates can support accurate brand timelines and “what’s new” style queries.
Next step
Make sure onsite press or announcements are clearly identifiable and consistently presented.
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 publication or update date shown
What we saw
We didn’t see a clear publication date or modification date displayed on the page.
Why this matters for AI SEO
Dates help AI systems judge whether information is current and safe to reuse, especially for topics where expectations and best practices change over time.
Next step
Add a clear publish date and, when relevant, an updated date that’s easy to find.
❌ Freshness within the last year couldn’t be confirmed
What we saw
Because there was no explicit updated date, we couldn’t confirm the content has been reviewed or refreshed within the last 12 months.
Why this matters for AI SEO
When freshness isn’t clear, AI systems may be less likely to surface the page for “current guidance” queries or may soften the language when summarizing it.
Next step
Include an updated-on signal when the content is reviewed so freshness is easy to verify.
❌ Sections are too thin to stand on their own
What we saw
The page is broken into sections, but the sections tend to be short and don’t consistently provide enough depth in one place.
Why this matters for AI SEO
AI systems extract answers in “chunks,” and thin sections can make it harder to lift a complete, confident answer without losing context.
Next step
Expand key sections so each one can deliver a more complete, self-contained explanation.
❌ No table-based summary found
What we saw
We didn’t find a table on the page.
Why this matters for AI SEO
Simple tables can make comparisons, checklists, and “at-a-glance” details easier for AI systems to parse and reuse accurately.
Next step
Add a small table where a quick comparison or checklist would naturally help the reader.
❌ Subheadings are too generic
What we saw
A lot of subheadings read as broad labels rather than specific, descriptive promises about what the section answers.
Why this matters for AI SEO
Descriptive subheadings help AI systems map questions to the right section quickly, which improves how reliably your content gets quoted or summarized.
Next step
Rewrite subheadings so they clearly reflect the specific question or takeaway each section covers.
❌ Key answers don’t show up early in sections
What we saw
The first paragraphs under major sections are very short, so they don’t deliver a strong “here’s the answer” moment up front.
Why this matters for AI SEO
AI systems often lean heavily on early text to understand what a section is saying. If the opening doesn’t contain a clear answer, the model may pull a weaker or less direct summary.
Next step
Make each section’s opening paragraph immediately state the core answer or takeaway in a more complete way.
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.
