BAX FOR AI
Other tools tell you if AI mentions your brand. BAX tells you if anyone actually pays attention when it does, which sources shape AI's opinion, and exactly how to change both.
Every competitor stops at Layer 1. BAX runs all four.
Most brands start by asking a simple question: does AI mention us?
AI model variants accessible
ChatGPT, Gemini, Claude, Perplexity, Grok, Copilot, Llama, Mistral, and hundreds more. Different models give different answers. Different versions of the same model give different answers. BAX captures every difference.
But visibility alone means nothing. A brand can appear in every AI response and still be ignored. The question that matters is whether the human reading that response actually paid attention.
BAX applies the Behavioral Biometrics Engine® to measure real cognitive attention on AI-generated content. Who reads the response. How deep they go. Which reader segments engage with your mention.
of attentive users reached by a brand mentioned first
reached when mentioned last. Position changes everything.
Once you know where you are visible and where humans pay attention, the next question is why. AI doesn't invent opinions. It inherits them from sources: your website, Wikipedia, review sites, industry portals, news articles, competitor content.
BAX maps the complete source ecosystem that shapes what AI believes about your brand. Trust Sphere shows which domains AI cites, how often, in what sentiment, and how your owned properties rank against external sources. If a competitor's blog outranks your official site in AI citations, that is not an AI problem. That is a source problem. And now you can see it.
Your own site is one of those sources, and often the weakest. AI crawlers read the raw page, not what your visitors see after scripts load. BAX tests every URL the way they read it: whether they can access it, whether expert credentials are machine-readable, and whether your content links to anything they can follow.
Seeing the problem is not enough. BAX turns every finding into a change your team can ship: structured data for your website, knowledge base files for AI crawlers, entity entries for Wikidata, correction briefs for publishers citing wrong information.
Changes arrive as a 90-day playbook split by role, for editorial and for SEO and IT. Every task has the URL, paste-ready text or code, where it goes in your CMS, and an acceptance test.
Then we prove it. The same questions run on day 0 and day 90, and changed pages are compared with a control group left untouched. That separates the effect of your work from changes in the models themselves. The loop closes. How an engagement runs →
ACCURACY
Typical first audit finds 5-15 critical factual errors across platforms. Wrong manufacturer, wrong founding date, wrong product portfolio. Each flagged with the exact AI response, the model, and the severity. For pharma and finance, this is not a feature. It is a requirement.
COMPETITION
BAX auto-detects competitors and benchmarks them alongside you. Share of voice, battle map per query, win/loss per model. You see not just whether you appear, but who AI recommends instead, which sources give them the advantage, and what it would take to change the outcome.
BAX FOR AI — COMMON QUESTIONS
BAX tracks brand mentions across 300+ AI model variants on 6 major platforms. For each mention BAX measures citation position, sentiment, narrative accuracy, and share-of-voice. A brand mentioned first reaches 77% of users. Mentioned last: 18%.
Citation depth measures how prominently a brand appears — whether it is the primary recommendation, a secondary mention, or a brief reference. BAX weights citation depth using the same positional decay model applied to web and video, calibrated at lambda 1.56 for AI surfaces.
BAX monitors whether AI platforms accurately describe a brand's products, pricing, and positioning. Each factual error reduces the BAX Index accuracy component (10% weight). BAX delivers ready-to-deploy correction packages to fix inaccurate AI narratives.
BAX applies the same positional decay model to AI response structure. A brand mention at the top reaches significantly more cognitive attention than one buried at the end. BAX also measures exposure quality through citation length, contextual prominence, and recommendation type.
BAX measures before any change goes live, then re-runs the same questions on day 90. Pages that were changed are compared with a control group of pages left untouched, so improvements caused by AI model updates are not counted as results of your work.