How is evidence strength measured?

Evidence Strength scores how specific, verifiable, and machine-extractable your claims are. Vague assertions like 'we help teams work better' score low. Specific claims like 'we reduced fulfillment errors by 34% in six months for Acme Manufacturing' score high. AI systems prefer claims they can verify and cite.

How is the Clarity Diagnostic scored?

The Clarity Index measures how accurately AI can read and trust your data across eight weighted factors: Context (20%), Accuracy (17%), Consistency (15%), Specificity (15%), Recency (12%), Machine Readability (8%), Hallucination Risk (7%), and Rendering Independence (6%). Each factor gets its own score from 1 to 5, with per-factor evidence showing exactly what AI found. The factor scores produce a composite Clarity score from 0 to 100.

What are the eight factors that determine the Clarity Index?

Context, Accuracy, Consistency, Specificity, Recency, Machine Readability, Rendering Independence, and Hallucination Risk. Each measures a different dimension of how well your data communicates trust to AI systems. A weakness in any factor drags down your overall score. A zero in any factor can trigger exclusion.

What Happens When You Type Your Own Company Name into ChatGPT

Open ChatGPT. Type Your Company Name. See What Comes Back.

Not a generic category query. Not “best dust collector for a small shop.” Your actual company name. The one on your business cards and the side of your building. Hit enter.

Most owners have never done this. They have checked Google. They know their rankings. They talk to customers and reps every day. But they have never asked the tool their buyers are actually using what it thinks about them.

In our 20-company audit, 7 of 20 had zero machine-readable evidence on their websites. AI systems reading those sites have nothing to work with. When a buyer asks about those companies, the AI guesses from third-party sources, and the guesses are often wrong.

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 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. Check whether ChatGPT uses your product names correctly. Check whether it identifies your primary market correctly. Check whether it mentions capabilities you actually offer, or fills 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 problem. Your Clarity Index determines how accurately AI systems represent you. A weak Clarity Index means inaccurate descriptions. Inaccurate descriptions mean buyers arrive expecting things you do not offer. Those buyers never request a quote.

Check Whether AI Places You in the Right Category

Ask: “Who are the leading companies that do [what you do] for [your market]?”

AI sometimes describes a company that builds industrial workholding as a “machine tool accessories supplier.” AI lumps a custom fabricator in with job shops that run production parts. 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 the best alternatives to your company name are. Write down every name that comes back. Cross-reference that list against your own competitive intelligence. Check whether the names are right, whether anyone is missing who should be there, and whether anyone appears who should not.

If a company that is not really a competitor keeps showing up in these queries, they have invested in their AI readiness 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 supplier 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, and Your Competitors Are Already Fixing It

If the descriptions are accurate and you show up in recommendations, your foundation is solid. Focus on the visitors AI sends you.

If the descriptions are wrong, the category is off, and you do not appear in recommendations, you have a Clarity problem that needs fixing before anything else matters. Your information is letting you down at the most basic level.

The Clarity Index is fixable. It is not about gaming algorithms or adding more pages to your site. 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 Corlarity 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.