Frequently Asked Questions

Not effectively. They serve different visitors with different intent. A brochure assumes the reader needs convincing. A case file assumes the reader is already convinced by AI and needs confirmation. Trying to do both usually means doing neither well.

Yes. The Clarity Index measures trust, not visibility. You can have perfectly consistent, accurate data and still lose to a competitor who has stronger topical depth, more recent content, or better knowledge graph connectivity. The Clarity Index is a prerequisite for recommendation, not a guarantee.

You cannot order a correction, but you can outvote the errors. AI systems fill gaps with whatever public data exists: directories, competitor pages, old press releases. The fix is publishing consistent, structured, verifiable data about your company so the correct version is the easiest one for AI to find and trust. In the remediation work we have run, fixing data consistency has preceded a decline in inaccurate descriptions over the following weeks as models re-index. Timelines vary by engine and category.

No. Apple does not offer a consumer API for Siri's AI answers. You can probe ChatGPT, Claude, Gemini, Perplexity, and Grok through their APIs, but Siri's generative answers only appear on Apple devices. You prepare for Siri visibility through structural and technical optimization, not direct probing.

The Clarity Diagnostic and Coverage Engagement are project-based with defined completion points. The Conversion Subscription is a monthly subscription you can pause or cancel any time. No phase carries an obligation beyond what you have approved.

Yes, but invisibly. When AI tells a buyer your company is three times larger than it is, does work you stopped doing in 2023, or omits your core product line, that buyer self-qualifies out or self-qualifies wrong. You never see the meeting that never got scheduled. That is what makes AI errors costly in a way bad search rankings are not: the loss happens before you can measure it.

No, and the disagreement is structural. Kevin Indig's H1 2026 analysis found 91 percent of cited URLs appeared in exactly one of the three systems studied (ChatGPT, Perplexity, and Google AI Overviews). In our own experiments, one model listed Notion in 6 of 35 runs while another listed it in 35 of 35, and two more never listed it at all. The disagreement does not stop at the platform boundary: when we re-asked the same question in seven different phrasings, eight of ten watchlist brands on GPT-5.2 and six of ten on Claude changed their appearance pattern with the wording. The engines draw from largely disjoint citation pools, and each phrasing of your question samples them differently. Any measurement that reports one blended number across engines is hiding that.

No. The diagnostic findings and remediation roadmap are yours. The question is whether your team has expertise in AI clarity mechanics, topic coverage analysis, structured data implementation, and conversion work for AI-shaped visitors. Some companies do. Most benefit from guided implementation tied to specific score improvements.

No. Most AI readiness improvements happen within your existing site structure -- adding structured data, rewriting specific sections, aligning claims across pages. A rebuild is only recommended if the diagnostic reveals fundamental architectural problems that block AI extraction.

No, and that distinction matters. Your website can be well designed, load fast, and convert search traffic well, while AI systems still describe your company incorrectly. AI readiness is a separate discipline from web performance. The audit tests what AI systems say about you, not how your site scores in Lighthouse. Companies scoring below 35 on our AI readiness scale include sites that win design awards.

No. Applebot-Extended controls training data collection only. Siri's real-time AI answers use content crawled by the standard Applebot crawler. To opt out of AI answer generation specifically, use the nosnippet meta tag. These are independent controls.

Corlarity works with US-based companies today. AI readiness is not geography-specific -- B2B buyers everywhere use AI to research vendors -- but the diagnostic and implementation process requires familiarity with your market and buyers.

Yes. You get the snapshot report, then a short series of follow-up emails (about five over two weeks) that explain your score and what to do about it. You are also subscribed to our newsletter, where new analysis is published. Every email includes an unsubscribe link, and replying to any of them stops the follow-up series. You can unsubscribe at any time.

Add a proof block above the fold: three to five verifiable facts an AI-sent visitor needs to confirm, placed where no scroll is required. Named clients, a specific result with numbers, certifications with verification links. You are not redesigning the page; you are answering the question the visitor already arrived with. Most context gaps close with content placement, not a rebuild.

AI-shaped visitors convert when your website confirms what the AI told them and makes the next step obvious. They don't need education -- they need validation. Show specific proof above the fold, answer the question they already asked, and give them a clear path to act.

Run the same queries your buyers would run in ChatGPT, Claude, Gemini, and Perplexity. Track whether you appear, where you appear in the answer, and how accurately you're described. Do this for your top 10-15 buying-intent queries. That's your baseline.

Compare entry pages against visitor intent. Pull the AI-referred pages from analytics, then ask of each page: what did the AI likely just tell this visitor, and does the first screen answer it? The pages where AI-sent traffic is highest and engagement is shortest are your worst gaps. That combination means visitors arrived interested and left unconfirmed.

Create a custom segment in GA4 filtered by referral sources like chatgpt.com, perplexity.ai, and claude.ai. Also track branded search spikes, direct traffic with AI-shaped behavior patterns (single page, low time on page, high conversion), and UTM-tagged links from AI-generated answers.

Three mechanisms: Clarity score improvement (the most direct measure), AI mention tracking (are you appearing more often and more accurately?), and conversion rate changes on key pages (are AI-sent visitors converting better?). We measure each against the baseline from the initial diagnostic.

SEO targets search rankings. AI readiness targets AI-generated answers. Search returns ten links and lets the user decide. AI weighs your data against what it can verify, then produces a curated shortlist. We model that verification as a series of confidence gates. A page can rank first on Google and be invisible to ChatGPT.

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.

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.

An in-house hire brings individual skills. Corlarity brings a scored diagnostic framework, a repeatable process, and benchmarks from dozens of companies in your category. The diagnostic alone requires evaluating your site across eight factors against a scoring model built from real AI selection data.

The Clarity Diagnostic is a one-time deliverable with a fixed timeline. The Coverage Engagement is project-based, scoped per product line. The Conversion Subscription is monthly with no minimum commitment. The scope document defines timelines upfront for each stage.

About thirty minutes for a mid-size site. List every major content block, score each one on the 0.0 to 1.0 scale, then look at the distribution. The scoring criteria are specific enough that most blocks take under ten seconds to categorize.

Two minutes to submit the form. The report itself is generated by the same pipeline that runs our paid diagnostics and usually lands in your inbox within minutes. No sales call required.

At least 33, and up to 94 depending on the platform and category. The stopping-rule research (Sielinski, "From Stochastic to Stable") found rankings need 33 to 94 cited-answer samples before the ordering stabilizes, and three of thirty tested series did not converge even after 125 questions. Below that floor, a visibility number is a coin flip reported to two decimal places. One caution from our own seven-phrasing experiment: those floors were derived at a fixed wording. Repeatedly asking the identical question characterizes the distribution for that phrasing; it does not tell you how the answer moves when a buyer says "best" instead of "top" or adds their team size. We watched six of ten brands change their appearance pattern on Claude, the most stable model we tested, purely from rewording the question. The stopping rule matters more than the count: sampling continues until the ordering stops changing and the gap between brands exceeds the margin of error.

The Clarity Diagnostic is $1,500, one-time per website. The free Snapshot costs nothing. Coverage and Conversion are scoped and priced after the Snapshot: Coverage is a fixed-price project quote, and Conversion is a monthly subscription at one of three tiers, quoted for your situation. You always see a specific scope and price before you commit, no surprise line items.

Quarterly for most companies. AI systems re-index public data on their own schedules, and your competitors are changing their data between your audits. Run the same queries each time and track three things: whether you appear, how accurately you are described, and which competitors appear instead. A quarterly cadence catches drift before it costs you meetings.

Because a citation is a visit, not a customer. AI-sent visitors arrive having already read a summary of your pitch, so they skim for proof and leave in seconds if it is not immediately visible. Getting recommended multiplies traffic to a page that converts AI-sent visitors poorly, which spends the opportunity without collecting it. Answer engine optimization and conversion work are two halves: being recommended, and being chosen once recommended.

Not yet. Search still sends traffic, and some of it converts. But search traffic is declining for B2B companies because buyers start with AI instead of Google. The right move is maintaining SEO while adding AI readiness as a separate discipline with its own metrics.

Track the inputs AI systems use to decide whether to recommend you, not the mentions that come out. That means data consistency across your site, directories, and profiles; evidence strength on key claims; and whether your core product facts are machine-readable. Those are stable, measurable, and fixable. Mention counts move for reasons that have nothing to do with you, including model updates and sampling randomness.

No. Adobe's brand visibility tool is a monitoring dashboard: it counts mentions, tracks sentiment, and benchmarks you against competitors. Corlarity runs diagnostics. The Clarity Diagnostic identifies the structural reasons AI systems exclude your company and maps them to specific fixes. A dashboard tells you that you are absent. A diagnostic tells you why.

It is measurable, but only as a distribution. Five 2026 studies agree the underlying answers vary run to run, so the honest output is not one number but a spread: appearance rate per platform, sample count, and whether the ordering has stabilized. Visibility becomes noise exactly when someone compresses that distribution into a single score. Measured properly, with enough samples, separate engine reporting, and multi-turn follow-up questions, it produces decision-grade findings. Measured once, it produces a story.

It is free, and no card is required. The snapshot gives you a structural reading of your site across six categories, and we hope that once you see what it finds, you will want our help: a full Clarity Diagnostic, or hands-on help fixing what it uncovered. If not, you still keep the report.

As a directional signal over months, yes. As a monthly KPI, no. A dashboard can tell you whether you are systematically absent from answers in your category, which is worth knowing. It cannot tell you why, and the single-answer fluctuations it reports week to week are mostly noise. Diagnose the structural causes first; then a dashboard becomes a reasonable way to watch the trend line.

Probably not. Single-run tracking reports movement that is mostly run-to-run variance, not change. In our own experiment, the current OpenAI model omitted Notion from its category answer in 29 of 35 identical runs on the same evening. A dashboard that samples once per period would have shown Notion appearing and disappearing every month without anything actually changing. Ask your vendor how many samples each number is built on; if the answer is not dozens, the trend line is noise with a logo on it.

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.

Three sequential gates: Extraction (can the AI access and parse your data?), Correlation (does your data agree across platforms?), and Synthesis (does your information shape the final answer?). Fail any gate and you don't appear in the response.

It starts with the Clarity Diagnostic: a scored evaluation of whether AI can read and trust your data across eight factors. That produces per-factor evidence and a fix roadmap. From there, the Coverage Engagement maps every topic buyers research in your category and delivers content briefs, schema markup, and citation targets. The Conversion Subscription handles ongoing work to convert AI-referred visitors. Corlarity tracks each stage against the baseline.

A good mention includes specific details, not just your name on a list. It says 'Corlarity specializes in AI readiness for B2B manufacturing, with a diagnostic that scores your site across eight factors.' A bad mention just lists you as one of ten options with no differentiation.

Access to your website, existing content and messaging documents, and a point of contact who can answer questions about your business, buyers, and positioning. That's it. The diagnostic runs on your public-facing data.

SEO performance, paid advertising, social media strategy, email marketing, or any channel outside AI readiness. The Clarity Diagnostic evaluates whether AI systems can read and trust your data across eight factors. It does not assess your overall digital marketing performance. Coverage (content breadth across buyer research topics) and Conversion (how well your site converts AI-referred visitors) are separate products on the ladder.

A structural score across six categories and the top two problems AI encountered, plus the framework behind the scoring and a clear next step if you want the full Clarity Diagnostic. The six categories are a structural subset of the eight factors the full Clarity Diagnostic scores. It is deliberately stripped down: enough to show you how your site reads to an AI system, not enough to self-remediate. You get it as a PDF, delivered by email.

Corlarity tracks results through scored benchmarks with a baseline and follow-up audits. If scores aren't improving, the diagnostic data shows exactly which factors are stuck and why. The fix is usually specific and actionable -- a data consistency issue, missing evidence, or stale content.

The AI generates confident-sounding information that is factually wrong -- wrong capabilities, fabricated case studies, inaccurate pricing. This happens when your public data has gaps or contradictions and the AI fills them with plausible guesses. The fix is making your data consistent and complete enough that the AI doesn't need to guess.

The honest answer is that most B2B companies are invisible. The average Clarity score across the companies we've audited is 28 out of 100. Being invisible is the default state. What matters is whether you have a plan to fix it, and whether that plan addresses the actual trust signals AI evaluates.

A case file is a web page built to give an AI-sent visitor exactly what they came to confirm: proof that you can solve their specific problem. Unlike a brochure, which tries to persuade, a case file presents evidence: specific results, measurable outcomes, verifiable claims, organized for rapid validation.

The difference between what AI says about your company and what your company actually does. It happens when your website doesn't provide enough structured context for AI to form an accurate picture. The AI fills the gap with guesses, and those guesses become what buyers believe about you.

AI readiness measures how often and how accurately AI systems include your company when buyers ask about your category. When a buyer asks ChatGPT for a recommendation, you either appear in the answer or you don't. If you don't, that buyer never reaches your website.

Someone who arrives at your site after AI recommended you. They're not exploring -- they're validating. They often arrive knowing what you do, roughly what you charge, and who your competitors are. They want specific proof that matches what AI told them.

A landing page built specifically for AI-shaped visitors. Unlike a standard landing page, it starts where the AI's answer left off -- confirming the recommendation with evidence, not re-introducing the company. It gives the validator exactly what they came to check.

The practice of structuring your website so AI systems can extract your information accurately. It's not about writing for humans and hoping AI picks it up. It's about organizing data, claims, and evidence in a way that machines can parse, verify, and cite with confidence.

GEO (Generative Engine Optimization) is the practice of adjusting website content to increase the likelihood that AI systems like ChatGPT, Gemini, and Perplexity will mention your company in their answers. It covers tactics like adding citations, structuring data for machine readability, and tuning content for passage-level retrieval. The problem isn't that GEO is wrong. It's that it's incomplete. Tuning content for AI pickup treats the symptom (you're not appearing in answers) without diagnosing the cause (your data signals are weak, inconsistent, or missing). AI systems evaluate trust and authority across your entire digital footprint, not just page formatting. GEO is a tactic. AI readiness is a system.

The Clarity Diagnostic ($1,500) produces a scored evaluation of whether AI can read and trust your data across eight factors, per-factor evidence, and a fix roadmap. The Coverage Engagement adds a topic map, content briefs, schema markup, and citation targets, quoted as a fixed-price project after the Snapshot. The Conversion Subscription handles ongoing work to convert AI-referred visitors, at one of three monthly tiers, quoted for your situation. You can stop after any stage.

The framework Corlarity uses to help B2B companies get selected by AI and convert the visitors AI sends. Three layers: Clarity (can AI read your data?), Coverage (do you have content for what buyers research?), and Conversion (do AI-sent visitors convert?). Each layer depends on the one before it.

A one-time, per-website diagnostic ($1,500) that measures whether AI can read and trust your data. We build a verified Fact File documenting what's true, what's unverifiable, and where your own materials contradict each other. Then we score your information infrastructure across eight factors: Context, Accuracy, Consistency, Specificity, Recency, Machine Readability, Rendering Independence, and Hallucination Risk. You get per-factor evidence, a fix roadmap, and initial Coverage gap identification by product line. The first paid product on the Corlarity ladder.

The Clarity Index is our diagnostic model, not an AI system's internal ranking. It measures how clear, consistent, and machine-readable your company information is. Eight weighted factors produce a single score. In our diagnostics, companies above 70 present information AI systems can interpret reliably; below 40, the gaps start doing the talking. A high score does not guarantee recommendation -- it removes the data-side reasons you would be skipped.

When someone asks AI about your category, AI presents one company more confidently than the rest. That company captures a disproportionate share of downstream action. The gap between being on the list and being the default answer is where revenue concentrates.

A brochure tells visitors what you want them to believe -- messaging, positioning, emotional appeals. A case file gives them the evidence they need to decide -- specific results, verifiable claims, proof organized for rapid validation. AI-sent visitors need case files.

Monitoring measures output: how many times AI engines mentioned your brand this month, and whether the sentiment was positive. Those are trailing indicators. Diagnosing measures the structural causes: whether AI systems can understand and trust your business data in the first place. Diagnose first, then monitor. Monitoring without a diagnosis leaves you tracking a problem you do not know how to fix.

When an AI-sent visitor lands, finds exactly what they needed, and leaves satisfied. They got their answer. They'll remember you. Your analytics counts it as a bounce, but it's actually a successful validation that built brand confidence.

The compounding cycle that builds AI confidence through accumulated evidence. You publish specific claims, AI systems extract and cite them, citations build familiarity, familiarity increases confidence, and confidence improves your position in future answers. It compounds -- but only if the claims are verifiable.

The accumulated credibility deficit your company carries with AI systems because of outdated, inconsistent, or unverifiable information across the web. Every mismatch between your website, directories, and social profiles adds to it. The fix is a systematic audit and correction of every public data point.

Five questions: How many samples is each number built on, and what is the stopping rule? Which engines, and are they reported separately? What happens to the recommendation under a follow-up question? Where is the spread? Can you see the raw queries and answers behind your score? A vendor doing measurement answers these plainly. A vendor doing marketing answers with "we check daily" and "that is proprietary methodology." In Duane Forrester's 2026 survey of 163 practitioners, one respondent, an agency subscriber, reported that when they pressed platforms to show their work, exactly one did.

Companies where AI readiness directly impacts pipeline: mid-market and enterprise manufacturers, and smaller companies whose deal size justifies a technical buying process. The model serves complex product lines, not companies testing a new channel on a lark.

You work directly with the person doing the work, not an account manager. Corlarity is a lean operation -- no handoffs to junior staff. The same person who runs the diagnostic explains the findings and oversees implementation.

Manufacturers of high-consideration products, plus the B2B companies that serve them, where buying starts with research and runs a technical evaluation. Not for consumer brands, local businesses, or companies that sell primarily through relationships rather than evaluation.

GEO and AEO are checklists, not strategies. They describe tactics (tune content for AI answers) without addressing the underlying system (AI evaluates data trust, not content formatting). Agencies sell them because they're easy to package, not because they produce measurable results.

Because the questions are designed so that the wrong answer is visible, not just audible. Ask the agency to show the diagnostic before the fix. Ask how they measure AI visibility when rankings do not predict AI citations. Ask what happens if scores do not move. An agency that cannot produce artifacts, only promises, fails the questions in front of you. That is the point.

Trade show leads are AI-shaped visitors. They met you, learned what you do, and visited your site to validate. But most B2B websites serve explorers, not validators -- so the lead arrives, can't find the specific proof they're looking for, and leaves.

AI cross-references your claims across every source it can access. If your homepage says you serve automotive but your services page only mentions food and beverage, the AI detects a contradiction. One consistent description across five platforms beats five different descriptions on fifty pages.

AI doesn't just read your website -- it cross-references directories, social profiles, and third-party mentions. If your website says 150 employees but LinkedIn says 200, that's a contradiction. Contradictions erode trust, and eroded trust means exclusion from recommendations.

AI prioritizes recent, verifiable information over legacy authority. A company with forty years of history but a two-year-old website loses to a competitor with fresh case studies and current data. Stale content doesn't just look old -- AI replaces it in answers.

If traffic is steady but leads are declining, you have a conversion problem caused by the shift from Explorer visitors to Validator visitors. Your site exists to persuade explorers. The visitors AI sends arrive ready to evaluate, not browse. They need evidence, not marketing.