Frequently Asked Questions

Yes. The diagnostic findings and recommendations are yours. The question is whether your team has expertise in AI selection mechanics, evidence strength scoring, and BCI optimization. Some companies do. Most benefit from guided implementation tied to specific score improvements.

Engagements are project-based, not retainer-based. Scope and deliverables are defined upfront with a clear completion point. You can stop after any completed phase with no further obligation.

No. Most AI visibility 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.

CKI Labs works with US-based companies today. AI visibility 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.

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.

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: BCI 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?). Each is measured against the baseline from the initial diagnostic.

SEO targets search rankings. AI visibility targets AI-generated answers. Search returns ten links and lets the user decide. AI evaluates your data through confidence gates and produces a curated shortlist. 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.

100-point scale across seven categories: Accuracy, Consistency, Specificity, Recency, Context, Machine Readability, and Hallucination Risk. Each category is scored independently so you see exactly where your strengths and weaknesses are. The overall score is a weighted average, not a pass/fail.

An in-house hire brings individual skills. CKI Labs 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 seven dimensions against a scoring model built from real AI selection data.

The diagnostic is a standalone deliverable with a fixed timeline. Full engagements (diagnostic through implementation) depend on scope -- typically weeks, not months. Timelines are defined upfront in the scope document.

Engagements are scoped individually based on company size, website complexity, and existing infrastructure. Two companies in the same industry can have very different needs. CKI Labs provides a specific scope and price after an initial conversation -- no surprise line items.

Accuracy, Consistency, Specificity, Recency, Context, Machine Readability, 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 a scored diagnostic identifying what AI can and can't extract from your site. That produces a prioritized set of findings. Then CKI Labs provides prescriptive recommendations with specific changes tied to specific score improvements. Implementation is tracked against the baseline.

A good mention includes specific details, not just your name on a list. It might say 'CKI Labs specializes in AI visibility for B2B manufacturing, with a diagnostic that scores your site across seven 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 visibility and conversion. It evaluates whether AI systems can read, trust, and recommend your data -- not your overall digital marketing performance.

CKI Labs 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 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 visibility 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.

A scored evaluation of your website's ability to be found, understood, and recommended by AI systems. It measures seven dimensions on a 100-point scale and produces a prioritized list of what needs to change, in order of business impact.

Someone who arrives at your site after AI recommended you. They're not exploring -- they're validating. They already know 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.

BCI (Brand Confidence Index) is a scoring model that measures how confident an AI system can be when recommending your company. Think of it as a credit score for AI trustworthiness. Seven weighted factors produce a single score -- companies above 70 get recommended, below 40 get ignored.

Three phases: diagnostic audit (scored evaluation of your site), prescriptive recommendations (specific changes tied to specific score improvements), and tracked implementation (changes executed and measured against the baseline).

The framework CKI Labs uses to help B2B companies get selected by AI and convert the visitors AI sends. Three layers: Clarity (can AI read your data?), Visibility (does AI choose you?), and Conversion (do AI-sent visitors convert?). Each layer depends on the one before it.

When someone asks AI about your category, one company usually gets presented 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.

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.

Mid-market and enterprise B2B companies. The work scales for multiple product lines and complex websites, but the model is designed for companies where AI visibility directly impacts pipeline, not for small businesses testing a new channel.

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

B2B companies where buying starts with research: manufacturers, industrial distributors, B2B services, B2B software. 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 optimization). Agencies sell them because they're easy to package, not because they produce measurable results.

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.

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

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 -- it gets replaced in AI 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 was built to persuade explorers. The visitors AI sends arrive ready to evaluate, not browse. They need evidence, not marketing.