The Language of AI Visibility

A technique where AI engines retrieve relevant documents in real-time to augment their responses. RAG is why fresh, well-structured content matters — it's not baked into the model, it's pulled live. If your content isn't RAG-friendly, it doesn't get cited.

When an AI engine generates information that isn't supported by its training data or retrieved sources. Can result in incorrect brand mentions, fabricated product claims, or confidently wrong recommendations. Grounding reduces hallucination risk.

The process by which AI engines verify generated answers against source material. Well-grounded responses include citations and factual claims; poorly grounded ones hallucinate. Your content's structure directly affects how well it can ground an AI response.

The corpus of text used to train an LLM. Content in training data may influence an AI engine's baseline knowledge about a brand, separate from real-time retrieval. Think of it as the model's "memory" before it looks anything up.

How variations in user queries affect which brands and sources an AI engine cites. The same question phrased differently can produce completely different brand recommendations. "Best CRM" and "CRM for small teams" might cite entirely different products.

AI-powered content creation workspace for producing AEO-optimized articles. Includes collaborative editing, section management, version history, and multiple export formats. Publication workflows from draft to live.

Bulk prompt execution campaign. Run hundreds of test queries across multiple AI engines simultaneously to measure visibility at scale. Think of it as a crawl, but for AI answers instead of web pages.

Product-level management and visibility tracking. Monitor individual SKUs across AI engines, track per-product visibility scores, and identify which products need attention versus which are already being recommended.

Monitor specific brands and competitors in real-time. Track their AI mention trends, citation changes, and competitive movements. Get alerted when a competitor's visibility shifts meaningfully.

AI-generated action plans based on your visibility data. Prioritized recommendations for on-site fixes, off-site opportunities, PR plays, social signals, and technical improvements — ranked by expected impact.

Analyzes which high-authority sources get cited alongside your brand in AI responses. Uses Tranco domain rankings to identify where you should be mentioned — and where you're missing. The citation network that matters for AI.

One dashboard for every team Normalizes data from different tools into a shared vocabulary so SEO, dev, marketing, content, and analytics teams all operate from the same definition of success.

Push fixes live — no dev tickets. Collapses the timeline from the typical 90-day sprint cycle to seconds. See a schema issue, fix it, deploy it. All from the same interface.

Skip the backlog, deploy directly The traditional workflow — Jira tickets, sprint slots, QA cycles, deployment windows — takes 90 days for a 30-second fix. Zero-Queue removes the queue entirely.

How content is organized to help AI engines understand topics, relationships, and hierarchy. Clean headings, logical sections, clear definitions before jargon. The better the structure, the easier it is for AI to extract and cite your content.

Structured data markup embedded in your pages that tells AI engines exactly what your content is about. Product specs, FAQ answers, organization details, article metadata — all in a format machines read natively. The technical backbone of AEO.

How AI engines identify and categorize brands, products, and topics within content. Strong entity recognition — consistent naming, clear definitions, structured markup — increases the likelihood of being cited correctly.

The degree to which AI engines recognize a domain as an expert source on a specific subject. Built through comprehensive, interlinked content clusters — not individual pages, but a body of work that signals deep expertise.

When an AI engine references a specific source (URL, brand, or content piece) in its generated answer. The AI equivalent of a backlink. Source attribution is the currency of AI visibility — no citation, no credit.

Indicators that help AI engines determine which sources to trust and cite. Includes domain expertise, content depth, editorial standards, authorship credentials, and third-party validation. The trust layer that separates cited sources from ignored ones.