You've checked your SEO dashboard. Your primary keywords are ranking on page one. Your organic traffic is steady. By every traditional metric, your B2B brand is winning.
So why does it feel like your lead quality is dropping?
AI visibility isn't a new channel to add to your stack. It's the score of everything you're already doing. When someone asks ChatGPT to recommend tools in your category, the answer reflects your entire digital footprint: your earned media, content, SEO structure, and community presence. Get the inputs right, and AI visibility is the natural output. Get them wrong, and you can rank on page one of Google and still be completely invisible to the buyer who matters most.
Why This Is Harder Than It Looks
AI search is now ordinary behavior. In a Brookings/NORC survey fielded in June 2025, 57% of US adults said they had used generative AI for at least one personal purpose, and about three quarters of those used it to search for information. On the buying side, Forrester's Buyer's Journey Survey, 2024 found that 95% of B2B buyers plan to use generative AI in at least one area of a future purchase, and over half say it led them to consider more or different vendors. Being in those answers is rarer than most founders assume. Walker Sands' benchmark of 828 enterprise B2B companies across 14 industries found that the median brand appeared in 3.0% of relevant Google AI Overviews, where relevant means keywords the brand already ranks for. If that is true of enterprise brands, a Series A startup cannot assume it is in the mix. The discovery phase, the moment a buyer is forming their shortlist, is happening without them.
If you aren't being cited during that window, you've lost the deal before it ever hits your CRM.
The Althea Score: A Diagnostic Across Four Inputs
The Althea Score is our proprietary four-part diagnostic that measures how visible a brand is across the inputs that AI models actually use to form recommendations.
At Althea Labs, a brand presence and AI visibility agency that builds targeted visibility systems for B2B startups through integrated PR, SEO, and content programs, AI visibility isn't something we bolt on at the end of a campaign. It's what we're building toward from day one, by getting the inputs right.
When we work with a new client, the first thing we do is establish a baseline across the four inputs of the Althea Score:
- Part 1: Front-End SEO. How your content shapes what AI engines say about your brand (this post)
- Part 2: Back-End Infrastructure. Whether the crawlers behind AI search can reach and read your pages
- Part 3: The PR Footprint. Why AI engines cite earned media, and how to measure your share of it
- Part 4: Community Presence. How community conversation on Reddit, forums and review sites shapes what AI engines say about a brand
One thing to keep in mind as you read the series: you can have exceptional content and still be invisible if the other three inputs are broken. Front-end SEO is one signal AI is reading. PR, technical infrastructure, and community presence are the others. All four matter. If you want the overview first, our guide to what AI visibility is covers the four inputs in one place.
So, What AI Is Actually Doing With Your Content
AI doesn't see your brand as a collection of keywords. It builds a picture of who you are based on everything it can find about you across the web: your owned content, your press mentions, your integrations, the problems you're associated with solving, how partners talk about you, your LinkedIn content, and more.
If your content hasn't clearly and consistently taught AI what you do, who you do it for, and why you're credible, the model fills in the gaps on its own. Sometimes that means pulling from outdated positioning. Sometimes it means ignoring you entirely and recommending a competitor who has done the work.
Imagine you sell a SOC 2 compliance tool to fintech startups, and a buyer prompts ChatGPT: "What's the best SOC 2 compliance tool for a fintech startup?" Your competitor's name comes up. Yours doesn't. Not because your product is worse, but because their content has done a better job of teaching AI exactly where they fit.
That's the gap this audit is designed to close.
Does AI Know What Your Brand Actually Does?
This is the question every front-end SEO audit should start with, and most brands are surprised by the answer.
Here's how we approach the content layer of an AI visibility audit, and how you can run the same diagnostic yourself: each of the four tests below gives you the exact prompt to type.
What We're Actually Evaluating
Before we run a single AI prompt, we assess the on-page infrastructure that either enables or undermines everything that follows. This is the part most SEO audits skip, or treat as a checklist rather than a diagnostic.
Metadata quality and duplication
We look at whether title tags and meta descriptions are unique, keyword-relevant, and properly scoped across the site. Duplicate title tags and meta descriptions are among the most common issues we find, and one of the most damaging. When multiple pages compete with identical metadata, search engines can't determine which to rank. AI models face the same problem: they can't build a coherent picture of a brand when its own pages contradict each other.
Topical authority and content depth
We assess whether the site has sufficient content coverage around the core problems the brand solves. This means looking at ranking keywords and average positions, yes, but also at semantic coverage. Does the content address the full range of questions a buyer might ask at every stage of a purchase decision? Thin content or content that exists only to rank, without actually answering anything, gives AI engines nothing worth citing. It just adds pages.
Schema and structured data
Google is explicit that structured data is not required for its AI features: its guide to generative AI features says structured data isn’t required for generative AI search, and its AI features documentation says there are no additional requirements to appear in AI Overviews or AI Mode. Even a page that meets every requirement is not guaranteed to be shown. We still treat Organization, Product, FAQ, and Article schema as hygiene, because validated structured data removes ambiguity about who you are, who you serve, and what you sell. Our view is that when schema is missing or broken, AI engines have to infer, and inference is where hallucinations start. We check for schema implementation, run validation tests, and flag anything that's giving the engines bad information.
On-page signals and internal architecture
We look at whether the site hierarchy clearly signals which pages matter most, whether internal linking supports topical clustering, and whether the content structure maps to the way buyers actually search. A site can rank and still fail to teach AI anything useful if its most important content is buried, orphaned, or written for a keyword rather than a customer.
The Four Diagnostic Tests We Run
Once we have a clear picture of the on-page foundation, we run a set of live AI prompts to understand how the models are interpreting that content in practice. You can run them yourself in ChatGPT, Gemini, Perplexity and Claude. Type your company's name wherever a prompt has square brackets, and swap our example category for your own.
The Baseline Perception Test
Prompt: "What is [your company] known for?" We put it to each of the leading models, ChatGPT, Gemini, Perplexity, Claude. Does the answer match your current positioning, or is AI recycling data from two years ago? Stale AI perception is a content problem, and it's fixable, but only once you know it exists.
The Category Association Check
Prompt: "What are the three best SOC 2 compliance tools for a fintech startup?" Swap in the core problem your company solves. Is the brand in the mix? If you're ranking first on Google but missing from these AI recommendations, your content isn't establishing you as a category leader. It's establishing you as a website.
The Positioning Accuracy and Hallucination Scan
Prompt: "What does [your company] cost, which tools does it integrate with, and which compliance certifications does it hold?" We check whether models are fabricating details: features that don't exist, incorrect pricing, compliance claims that aren't true, or problems that were fixed months ago. If an AI is repeating outdated information, it usually means nothing current and clearly stated is available for it to retrieve, so it falls back on older data. This is one of the most overlooked risks in B2B AI visibility, and one of the most damaging.
The Sentiment and Narrative Analysis
Prompt: "What are the pros and cons of [your company]?" We run variations of this to surface the pros and cons AI associates with a brand. What narrative is being built in the background? Is it the story you'd tell, or one you'd push back on? Understanding the AI-constructed narrative is the first step to correcting it.
SEO Isn't Going Away. It's Evolving
Traditional SEO is no longer just for humans searching on the web. It's the input layer that corrects AI hallucinations and cements your brand narrative in the answers your buyers are now using as a first stop in their research.
The on-page decisions you make, what to write, how to structure it, what schema to implement, which pages to prioritize, are the same decisions that determine whether AI models recommend you or skip you. The tactics have evolved. The discipline hasn't.
Every day you wait to understand how your brand is being represented in these systems is another day your competitors get to define the narrative for you. Algorithms change. Your brand identity shouldn't have to.
Coming Up in Part 2
We go under the hood to look at Back-End Infrastructure: how robots.txt, schema markup and page structure decide whether AI crawlers can find and read the truth about your brand.
Ready to See Where You Stand?
Every Althea Labs engagement starts with an audit. We show you exactly where your brand stands in search, in AI, and in the communities where your buyers actually are, then show you what it would take to build from there.
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