Open ChatGPT. Type Your Company Name. See What Comes Back.
Not a generic category query. Not “best project management software.” Your actual company name. The one on your business cards. Hit enter.
Most marketing leaders have never done this. They have checked Google. They have checked their SEO rankings. They have read the competitive landscape reports. But they have never asked the tool their buyers are actually using what it thinks about them.
This takes fifteen minutes. Grab a notebook.
Read the Description and Check It Against Your Homepage
Does ChatGPT describe your company accurately? Compare the description to your homepage. Compare it to your about us page. Compare it to how your sales team describes what you do.
If the three match, you are in better shape than most. Pay attention to the specific language. Does ChatGPT use your product names correctly? Does it identify your primary market segment? Does it mention capabilities you actually offer, or is it filling in gaps with plausible-sounding guesses?
This is where hallucinations show up. AI systems do not have a “not sure” setting for company descriptions. They generate something confident either way. If your data is not specific enough to anchor the description, the system invents details that feel right but are not true.
We call this a Clarity Index problem. Your Clarity Index determines how accurately AI systems represent you. Weak Clarity Index means inaccurate descriptions. Inaccurate descriptions mean visitors arrive expecting things you do not offer. Those visitors bounce.
Check Whether AI Places You in the Right Category
Ask: “Who are the leading companies that do [what you do] for [your target market]?”
A company that sells industrial IoT sensors sometimes gets described as a “smart home technology company.” A B2B logistics platform gets categorized with consumer shipping apps. The capabilities overlap enough that the AI makes a reasonable guess, but the guess is wrong.
Wrong category placement is a Context problem. Your information is not connected to the right nodes in the knowledge graph. AI systems find you through the associations you have built, and if those associations point to the wrong neighborhood, you will keep getting visitors who are not your buyers.
Run a Displacement Probe to See Who AI Considers Your Competition
Ask: “Who are the best alternatives to [your company name]?” Write down every name that comes back. Cross-reference that list against your own competitive intelligence. Are the names right? Is anyone missing who should be there? Is anyone listed who should not be?
If a company that is not really a competitor keeps showing up in these queries, they have invested in their AI findability more than you have. Their information is more specific, more current, better connected. AI trusts them more in your category than it trusts you.
Run a Recommendation Probe Without Naming Yourself
Ask: “I am looking for [your category] that does [your specific capability]. What should I use?” Do not name yourself. See if you show up at all.
This is the moment that matters most. This is what your buyers are doing. They are asking AI to recommend a solution for their problem, and AI is building a shortlist from its confidence scores. If you are not on that list, you have a Coverage problem. Your buyers are choosing from a shortlist you are not on.
Note your tier position. Were you mentioned in passing? Were you recommended with reasons? Or were you the default answer, the first name out of the gate? Three tiers, three different levels of business impact.
Repeat in Claude and Gemini to Find Cross-Platform Gaps
Different AI systems weight information differently. One describes you accurately while another gets your category wrong. One includes you in recommendations while another omits you entirely. These inconsistencies tell you where your information infrastructure has gaps.
Consistent results across platforms mean a strong Clarity Index. Inconsistent results mean your data is strong in some channels and weak in others.
The Clarity Index Is Fixable, but Your Competitors Are Already Fixing It
If the descriptions are accurate and you show up in recommendations, your foundation is solid. Focus on optimizing your validation architecture for the visitors AI sends you.
If the descriptions are wrong, the category is off, and you are missing from recommendations, you have a Clarity Index problem that needs fixing before anything else matters. Your information infrastructure is letting you down at the most basic level.
The Clarity Index is fixable. It is not about gaming algorithms or generating more content. It is about making your existing information more accurate, more consistent, more specific, more current, and better connected. Structured work. Measurable progress. That is exactly what CKI Labs does: measure your AI presence, find the gaps, and fix them with specific, measurable work. The question is whether you start before your competitors widen the gap further.
