How GEO Autopilot works
GEO Autopilot is a pipeline of ten stages that runs on a schedule, each stage producing evidence the next one uses. Nothing in it is a black box: every run has an id, every claim has a source, every change has a backup and a diff, and every metric is either measured or explicitly reported as not-yet-measurable. This page walks through the loop stage by stage.
1. Crawl — see your site the way machines do
A polite, rate-limited crawler inventories every reachable page: status codes, titles, headings, links, canonical tags, structured data, render weight. It respects robots.txt and never hammers your server. The result is a complete inventory the rest of the pipeline works from.
2. Audit — technical eligibility, per crawler
Thirty-plus checks answer one question: can search and AI crawlers access, interpret and index this site? Alongside the classic checks (sitemaps, canonicals, redirects, metadata, structured-data validity), the audit builds a crawler policy matrix: for each relevant bot — Googlebot, Bingbot, GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and more — whether your robots policy allows it, blocks it, or blocks it by accident.
3. Entity mapping — establish the facts
The system extracts who you are from your own site: brand name, offerings, locations, contact points, and every stated fact about the business. These go into a source-of-truth registry with a source, a verification date and a confidence score. This registry is what keeps later stages honest — content is assembled from verified facts, never invented.
4. Prompt universe — the questions that matter
Starting from your offerings and audience, the system builds intent clusters: the questions real buyers ask AI assistants, each with a seed query, fan-out sub-questions (the way engines decompose questions internally), phrasing variants, funnel stage, and the answer format engines prefer for that intent. This becomes both the test set for benchmarking and the demand map for content planning.
5. Scoring & opportunities — where to act first
Every page is scored on ten factors covering answerability, evidence density, entity clarity, freshness and technical health. Cross-referencing scores against the prompt universe yields a ranked opportunity list: intents no page addresses, pages one fix away from citable, and technical blockers with sitewide impact.
6. Produce — briefs and the claim ledger
For each unanswered intent, the system generates a content brief: a complete work order specifying target prompts, required structure, internal links, schema, and — critically — the evidence requirements. Every material claim a future page will make must first exist as a row in the claim ledger with a source and verification date. Briefs demand unique information gain (first-party data, tested procedures, transparent methodology) and real, named authors. Rewritten commodity content is explicitly forbidden — engines have no reason to cite the hundredth copy of the same advice.
7. Benchmark — ask the engines and record the answers
The prompt universe is tested against AI platforms on a budget-capped schedule. For every prompt we record the full answer, which URLs it cited, whether your domain was among them, whether your brand was mentioned, and how you were characterized. Detection of citations and mentions is deterministic string and domain matching — never a model's guess. Over time this yields the metric that matters: citation share per buyer question.
8. Publish — gated changes with verified rollback
Low-risk technical fixes (robots policy, sitemaps, metadata, structured data) can ship automatically, but only through a gate: current state recorded → backup written → diff generated → quality checks passed → change applied → live URL re-fetched and verified. If live verification fails, the change rolls back automatically. Anything the gate cannot approve becomes a ready-to-apply patch for human review instead. Every change is reversible with one command.
9. Measure — a five-stage funnel, no fake scores
AI visibility is reported as five separately-measured stages:
- Crawlability — can engines fetch you? (measured from server behavior and policy)
- Indexation — are you in the indexes engines retrieve from?
- Retrieval — do you appear in the sources engines consider?
- Citation — are you cited or recommended in actual answers?
- Conversion — do AI-referred visitors arrive and act?
Each stage is timestamped against a baseline taken before work began. A stage that cannot be measured yet (for example, before analytics access is connected) is reported as exactly that — never estimated, never blended into a made-up composite score.
10. Monitor — protect what was won
Daily checks watch for uptime problems, robots or noindex regressions, and facts approaching their review date. Weekly benchmarks track answer drift. Monthly full audits catch what daily checks cannot. Every run ends in an executive report: what changed, what was measured, what is blocked and by what, and what happens next.
The safety posture, in one paragraph
Dry-run is the default everywhere — the system proposes and shows diffs unless production mode is explicitly enabled. Applied changes require a backup and pass live verification or roll back on their own. Budgets cap crawl rate, content volume and benchmarking spend per run. And the claim ledger means the system cannot fabricate facts about your business even in fully automatic mode: if a fact is not verified, it does not ship.