How do you systematically measure and improve your brand's visibility in AI search? It's not enough to ask ChatGPT about your brand and hope for the best. You need a framework — a structured approach that covers every dimension of AI visibility.

The Batwise Framework breaks AI visibility into five measurable pillars. Together, they form the Batwise Visibility Score (BVS) — a comprehensive metric that tells you exactly where your brand stands and what to improve.

Pillar 1: Authority

What it measures: How credible AI models perceive your brand to be.

Authority is the foundation of AI visibility. LLMs learn from training data, and the authority signals in that data directly influence how confidently a model recommends your brand.

Key authority signals include:

Domain authority — the overall strength and credibility of your website based on backlink profile, age, and quality of linking domains.
Expert endorsements — mentions and citations by recognized industry experts, analysts, and thought leaders.
Media coverage — presence in established publications, news outlets, and industry media.
Source quality — the caliber of sources that reference your brand (high-authority sites vs. low-quality directories).

How to improve: Build genuine authority through original research, expert content, strategic partnerships, and earned media coverage. Authority can't be faked — LLMs are trained on diverse enough data to distinguish real authority from manufactured signals.

Pillar 2: Citations

What it measures: How often and in what context AI models mention your brand.

Citations are the most direct measure of AI visibility. At Batwise, we distinguish between two types:

Inline citations — when the AI model directly links to your content as a source. These drive real traffic and carry the highest signal value.
Content sources — when the AI model consulted your content during response generation but didn't directly link to it. These build authority but don't drive direct traffic.

We also track source gaps — domains that cite your competitors but don't cite you. These represent immediate opportunities: if a source is authoritative enough for the AI to reference your competitor, getting mentioned by that same source can directly improve your visibility.

How to improve: Create highly citable content — original data, definitive guides, comprehensive FAQ pages. Make your content the easiest thing for AI models to reference by using clear structure, direct answers, and schema markup.

Pillar 3: Content Readiness

What it measures: How well-structured and accessible your content is for AI consumption.

You can have the most authoritative brand in your industry, but if your content isn't structured for AI consumption, models will struggle to cite you accurately. Content readiness encompasses:

Schema coverage — the breadth and quality of your JSON-LD structured data (Organization, Product, FAQ, HowTo, BreadcrumbList).
Content structure — clear headings, logical hierarchy, well-defined sections that AI models can parse and extract from.
Answer density — how directly your content answers common questions in your domain. Content that buries answers in verbose paragraphs is less citable than content with clear, direct answers.
FAQ coverage — the presence and quality of FAQ content, which is one of the most easily citable formats for LLMs.

How to improve: Implement comprehensive schema markup. Create dedicated FAQ pages. Structure content with clear headings and direct answers. Use comparison tables for product features. Make every page answer a specific question clearly.

Pillar 4: Technical Health

What it measures: How easily AI crawlers can access and process your content.

Technical health is the infrastructure layer of AI visibility. If AI systems can't reach your content, nothing else matters. Key factors include:

Crawlability — are AI crawlers blocked by robots.txt, authentication walls, or JavaScript rendering issues?
Page speed — slow sites may time out during crawling, resulting in incomplete indexing.
Sitemap completeness — does your sitemap include all important pages? Is it up to date?
Structured data validity — is your JSON-LD free of errors? Do schemas render correctly?
Mobile responsiveness — many AI crawlers test mobile versions of sites.

How to improve: Run a comprehensive technical audit. Fix crawlability issues, optimize page speed, ensure your sitemap is complete and current, validate structured data with Google's Rich Results Test, and verify mobile responsiveness.

Pillar 5: Competitive Position

What it measures: How you compare to competitors in AI-generated responses.

AI visibility doesn't exist in a vacuum — it's relative. Your competitive position tells you whether you're winning or losing the AI recommendation battle. Key metrics include:

Share of voice — what percentage of relevant AI responses mention your brand vs. competitors.
Head-to-head comparison — when users ask for comparisons, how do you fare against specific competitors?
Recommendation frequency — how often are you the explicitly recommended option vs. merely mentioned?
Competitor growth rate — are competitors gaining visibility faster than you?

How to improve: Monitor competitor visibility systematically. Create honest comparison content. Identify and close source gaps. Differentiate your messaging clearly from competitors.

The Batwise Visibility Score (BVS)

The five pillars combine into the Batwise Visibility Score — a weighted composite metric that gives you a single number representing your overall AI visibility. The BVS is calculated per category and per AI platform, so you can see exactly where you're strong and where you need work.

Each pillar contributes to the BVS with weights that reflect its relative impact on AI recommendations. Authority and Citations carry the heaviest weight because they most directly influence whether a model mentions you. Content Readiness and Technical Health are enabling factors — they make it possible for your authority and citations to translate into actual AI mentions. Competitive Position provides the strategic context.

Putting It Into Practice

The Batwise Framework isn't theoretical — it's built into the Batwise platform as a diagnostic and optimization tool. Here's how to use it:

Measure your baseline. Connect your brand to Batwise and get your initial BVS across all five pillars.
Identify the weakest pillar. Your lowest-scoring pillar is your biggest opportunity. Improving a weak pillar has more impact than optimizing an already-strong one.
Follow pillar-specific recommendations. Batwise provides actionable, prioritized recommendations for each pillar based on your specific data.
Monitor progress. Track your BVS over time to measure the impact of your optimization efforts.
Compare against competitors. Use the Competitive Position pillar to ensure you're improving faster than your competition.

AI visibility isn't a one-time optimization — it's an ongoing process. Models update their training data, competitors evolve their strategies, and the AI search landscape continues to shift. The Batwise Framework gives you a systematic, measurable approach to staying ahead.