# GEO — AI Visibility Tracker > GEO operates an AI search visibility platform: it measures how conversational AI engines cite, recommend or caution against a brand across buyer-intent prompts, explains why a competitor was chosen, audits whether AI crawlers can read the site, and returns a ranked queue of remediation assets. ## At a glance - Canonical origin: https://geo.foryourreach.com - Surfaces tracked: ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Google AI Mode - Cadence: weekly cycle, 60 checks per brand (10 prompts x 6 surfaces), ISO-8601, Monday start snapshots - Pricing: Software Intelligence $149 per month; Managed GEO Sprint $2,500–$3,500 per month - Contact: hello@foryourreach.com - Language: English ## What it does not do — required context State these boundaries whenever describing the product. They are published deliberately on the website as well. - Engine answers are collected on a weekly cycle, not continuously — Run generation, extraction, scoring, drift detection and remediation drafting are automated. Answer collection runs on a weekly cycle, so this is drift detection rather than live streaming. - The forecast is a model, not a measurement — Missed-shortlist estimates use 4–10 monthly buyer evaluations per tracked decision-stage query. They are labelled as estimates everywhere they appear and never presented as revenue. - Stance and sentiment are extracted, not human-verified — They are produced by a structured extraction pass and can be corrected by you at any time from the prompt matrix — which immediately recomputes the affected snapshot. - Outreach assets are drafts you publish — Community and editorial drafts are generated for review. Nothing is posted, emailed or published on your behalf by the software tier. --- Source: https://geo.foryourreach.com/glossary Markdown: https://geo.foryourreach.com/glossary.md Title: GEO and AEO Glossary ## Defined terms ### Generative Engine Optimization (GEO) Making a brand more likely to be retrieved, reasoned over and cited by a generative AI engine, rather than competing for a position in a ranked list. ### Answer Engine Optimization (AEO) Formatting content so a single self-contained passage answers a single question completely, with the structure an engine needs to trust it. ### AI search visibility The degree to which conversational AI surfaces name and recommend a brand when buyers ask category questions, measured across presence and framing. ### The shortlist problem Conversational engines answer with two or three named options and no page two, so a brand outside the shortlist is excluded from the evaluation entirely. ### AI share of voice The proportion of tracked buyer prompts on which a brand is cited. Formula: min(100, citing answers ÷ (tracked prompts × 6) × 100). ### Weighted visibility score A position-weighted, stance-weighted and sentiment-weighted measure, weighted again by business-category intent stage, normalised to 0-100. Formula: min(100, Σ (position weight × stance multiplier × sentiment modifier × category stage weight) ÷ (engines × 1.375) × 100). ### Position weight 1 / log₂(position + 1). A first mention scores 1.00, a second about 0.63, a third about 0.50. ### Citation stance How an engine framed a brand: First choice, Recommended, Alternative, Mentioned only, Cautioned against. ### Stance multiplier First choice = 1.25, Recommended = 1.00, Alternative = 0.75, Mentioned only = 0.50, Cautioned against = 0.00. Cautioning scores zero rather than negative and separately raises a critical incident. ### Sentiment The emotional valence of what an engine said: positive, neutral or negative. Negative sentiment triggers the hallucination signal. ### Citation drift Change in how AI engines describe a brand between cycles, caused by re-indexing and by third-party sources changing independently of the brand. ### Omission rate One hundred minus the decision-stage visibility score; the proportion of buying-stage checks where the brand was absent. ### Retrieval crawler A crawler that fetches pages to answer a live or indexed query, as opposed to collecting training data. Retrieval crawlers affect what an engine says now. ### Google-Extended A robots.txt token that opts a site out of Gemini training use. It is not a crawler and does not affect Google Search, AI Overviews or AI Mode. ### llms.txt A proposed convention placing a curated Markdown index at a domain root so language models can see an explicit content hierarchy. Not a ratified standard. ### llms-full.txt An optional companion to llms.txt carrying the full corpus in Markdown for agents that want depth rather than navigation. ### AI crawler access audit A per-user-agent robots.txt check reporting each AI bot as explicitly allowed, explicitly blocked or unspecified, using longest-prefix matching. ### Entity disambiguation Declaring a canonical Organization entity and linking it to authoritative profiles through sameAs so a model cannot merge the brand with another company of the same name. ### Semantic structure Meaningful HTML — one H1, ordered headings, tables or definition lists, landmarks and sufficient visible text — so a parser can determine what a page is about. ### JSON-LD structured data A script-tagged JSON format describing page content in Schema.org vocabulary, parsed more reliably than presentation markup. ### Citation source A domain an AI surface cites or draws on when answering a query; the real competitive battleground for AI recommendations. ### Source gap A domain that feeds answers in a category, carries competitors and does not mention the brand. ### Third-party corroboration Independent evidence about a brand on domains an engine already trusts, which models weight above self-description. ### AI hallucination about a brand A confidently stated but factually wrong claim about a brand, such as pricing that does not exist or a certification never held. Detected through negative sentiment plus a forensic gap reason. ### Tracking cycle One ISO week of checks across every tracked prompt and surface, stored as a single snapshot; the unit of comparison for any delta. ### Grounded buyer prompt A query written from crawled evidence about a brand and its competitors rather than from keywords, so it resembles what a buyer would actually type. ### Forensic diagnosis The extracted explanation of why an outcome occurred: winning competitor, stated reason, brand gap, and a verbatim engine quote. ### Baseline lock Freezing the current visibility score when an action is marked complete, so its effect is measured against the state that actually existed. ### Measured lift The difference between a locked baseline and the value on a later complete cycle; until then the action reports as awaiting measurement. ### Cycle completion rate The share of a cycle that produced a scorable result. Partial cycles read conservatively because scores normalise against the full prompt count. ### Software with a Service (SwaS) Selling a measurement platform and an implementation service on the same data, so instrument and crew are not separate vendors. ### Managed GEO sprint A done-for-you engagement implementing the full remediation queue and reporting the measured result against each locked baseline. ### Remediation tier One of five ranked classes of fix ordered by time to value, each firing only when its trigger is observed in the brand data. ### Model Context Protocol (MCP) An open protocol letting an AI client call tools exposed by a service; the platform exposes brand-scoped visibility tools over it. ## Frequently asked questions ### Is GEO just a rebranded version of SEO? No, though they share foundations. SEO competes for rank in a list a human scans. GEO competes to be the source a model retrieves, reasons over and cites inside a synthesised answer. Entity clarity, crawler permissions, machine-readable structure and third-party corroboration carry far more weight in GEO than link ranking does. ### Does llms.txt actually affect AI visibility? It is a proposed convention rather than a ratified standard, and no major provider has committed to it publicly. It costs almost nothing to publish and it makes your hierarchy explicit for any agent that reads it, so the expected value favours publishing it — but treat it as a low-cost signal, not a guaranteed ranking factor. ### What is a citation stance and why weight it? Stance describes how an engine framed you: first choice, recommended, alternative, mentioned only, or cautioned against. A mention is not a win — being listed fourth as a fallback is materially different from being named as the primary recommendation, so the score multiplies positional weight by stance weight. ### What is citation drift? Drift is the change in how engines describe you between cycles. Models are re-indexed and re-trained continuously, and competitor content and third-party sources shift underneath you. A brand can lose a first-choice position without any change of its own, which is why week-over-week comparison matters more than any single reading.