# 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/how-it-works Markdown: https://geo.foryourreach.com/how-it-works.md Title: How It Works — The Weekly AI Visibility Operating Loop ## What happens in a normal week We read the customer’s website and their competitors once, then write ten buyer questions. Every week those questions go to six AI assistants and the answers are recorded. Any gap becomes a drafted fix, and once a fix is completed the score is frozen so the change can be shown the following week. ## The steps, in order - Reconnaissance — a bounded parallel crawl of the brand site, each competitor site, and any llms.txt file, under a hard latency ceiling with no retry storms. Unreachable pages degrade prompt grounding gracefully. - Grounding — ten buyer prompts across awareness, consideration and decision stages, written from crawled evidence rather than from keywords. - Cycle — sixty checks generated per brand per ISO week, idempotently, safe to re-run behind any scheduler. - Collection — each raw answer preserved verbatim before parsing; unusable answers flagged rather than scored as misses. - Extraction — stance, sentiment, position, sources and a forensic block per answer, with corrections available afterwards. - Scoring — one atomic snapshot per ISO week containing the weighted visibility score, citation share and stance strength. - Detection — five severity-ranked drift signals, at most one incident per check, most severe first. - Remediation — five tiers ranked by time to value, generated only when their trigger is observed. - Proof — baseline locked at completion, measured after-value written on the next complete cycle. ## What runs automatically - Crawl of the brand site and competitor sites — once, at onboarding. - Generation of ten grounded buyer prompts — once, at onboarding. - Creation of the next cycle of checks — weekly, idempotent. - Preservation of every raw answer — on collection. - Extraction of stance, sentiment, position and sources — on every answer. - Recomputation of the weekly snapshot — on every write and correction. - Detection and ranking of drift incidents — on every answer and weekly. - Drafting of ranked remediation assets — on every detected gap. - Batched alert digest — weekly, subject to a cooldown. Publishing assets and seeding citations are human steps. The platform is a measurement and drafting system, not an autonomous publisher. ## How impact is proven - The baseline is frozen when an action is marked complete and cannot drift afterwards. - The after-value comes from a real later weekly snapshot; until then the action reports as awaiting measurement. - Macro drift is compared only between two complete weeks, so a partial week cannot produce a false alarm. - Corrections recompute the affected snapshot in place, keeping historical comparisons consistent. ## Frequently asked questions ### What happens during onboarding? A bounded parallel crawl reads your homepage, pricing page, features or documentation and llms.txt, plus the same pages on each competitor, under a hard latency ceiling. That grounding produces ten realistic buyer prompts across awareness, consideration and decision stages, and opens sixty checks across six surfaces. ### How long until the first useful reading? The first snapshot exists as soon as the first cycle of answers has been extracted and scored, so you see real stance and citation data from week one. Trend, drift detection and measured lift need a second complete week, because a delta requires a prior point to compare against. ### How is impact proven rather than asserted? Marking an action done locks the current visibility score as its baseline. When the next weekly snapshot is computed, the same action receives a measured after-value, and the impact surface shows the before and after with the delta. Actions without a completed second cycle report as awaiting measurement rather than as a win. ### What runs each week automatically? An idempotent weekly rollover creates the next cycle of checks for every active brand, runs the macro drift check against the two most recent complete weeks, and dispatches the alert digest. Re-running it inserts nothing, so it is safe behind a scheduler or a retry. ### What do the engines see when they read your own site? The same standard we hold you to. This site publishes llms.txt, llms-full.txt and a capability manifest, allows retrieval crawlers explicitly in robots.txt, ships JSON-LD for Organization, WebSite, SoftwareApplication, FAQPage, HowTo and DefinedTermSet, and renders every page server-side with no client-side hydration.