- Measure what AI answers say about you. Bluemoon maintains a library of tracked questions for your domain, asks them on ChatGPT, Perplexity, Claude, Gemini, Google AI Overview, Google AI Mode, and Microsoft Copilot — the set depends on your plan — records the answers, and reports whether your brand appeared — broken down by platform, topic, persona, and the shape of the question.
- Control what AI crawlers see. The Agent Edge Network (AEN) is middleware you install in your own app. It verifies AI crawler identity at the edge and serves a citation-optimized version of the requested page to verified answer-engine agents.
Measure what AI answers say about you
You cannot query an answer engine’s index the way you query a rank tracker. The only way to know what ChatGPT says about your category is to ask it and read the answer. That is what Bluemoon does, on a schedule, and it records every answer so the numbers stay checkable. Ask — During onboarding, Bluemoon builds a library of tracked questions for your domain: the questions a real buyer would type, grouped into topics and tagged with a buyer-proximity tier. Questions are deliberately brand-free, because a question that names your brand will always produce an answer that names your brand. Record — Every tracked question is sent to each active AI platform roughly once a day, and the full answer is stored. When a platform cannot be reached, Bluemoon records the failure with a reason rather than writing a blank answer — nothing is ever fabricated to fill a gap. Score — Each stored answer is scanned for your brand and for each tracked competitor, and classified as a direct mention, an implicit mention, or absent. Cited URLs are extracted separately. From those classifications come the mention rate, share of voice, per-platform and per-topic breakdowns, sentiment, and the citation records.On the question-shape and commercial-reach surfaces, a rate with no measurement behind it is shown as a dash rather than 0%, and a measured but tiny rate that would round to 0.0% is shown as
<0.1%. “We asked and you were not mentioned” and “we have not asked yet” are different findings and are rendered differently. The headline Visibility tile does not yet follow this convention: it prints 0.0% for an empty slice.Control what AI crawlers see
Most brands have no idea whether AI answer engines are crawling their site, let alone whether those crawls turn into citations. The Agent Edge Network (AEN) closes that gap across three layers. Detect — AEN is an edge drop-in you install in your own app (Next.js, Cloudflare, WordPress, or a snippet for any other stack). It inspects every incoming request and matches AI crawlers against a maintained registry of bots. Each match is verified against the bot’s published IP ranges (CIDR) and, when present, a cryptographic Web Bot Auth signature — so you know which bot visited, at what trust level, and with what purpose. Serve — When a request is verified, AEN serves a pre-generated, citation-optimized artifact of the requested page instead of your standard HTML. Artifacts are answer-first markdown paired withArticle (and, when the page’s own headings are questions, FAQPage) JSON-LD. They are built offline by pure extraction and restructuring of your existing page — never fabricated — and cached, so nothing is generated on the request path and real users are unaffected.
Measure — Agent Analytics surfaces verified crawl hits broken down by purpose class and verification level, plus a served-versus-cited view: of the URLs you served a variant for, how many later appear as citations. A per-page citability score tells you where to focus.
Bluemoon never bot-branches traditional search crawlers. Googlebot, Bingbot, and Applebot always receive your standard page — the AEO variant is reserved for verified answer-engine agents. This cloaking firewall is non-negotiable and enforced in code.
Key concepts
Tracked questions
The brand-free question library Bluemoon asks on your behalf, grouped into topics and tagged with a buyer-proximity tier: core, adjacent, broad, or peripheral. Peripheral topics are generated as calibration ballast — questions so wide that no brand in your category would be named. Without them a visibility score drifts toward 100% and stops meaning anything.
Visibility (mention rate)
The share of recorded answers in which your brand appeared, counting direct and implicit mentions. Questions that name your own brand are excluded from the competitive comparison, because an answer to a branded question always names you.
Question shape
What the question asks the AI to do — a definition, a how-to, a comparison, a best-of list, an alternatives list, pricing, a review, a recommendation, troubleshooting, or availability. Mention rates differ sharply by shape, which makes it the axis you can act on.
Commercial reach
The same mentions, weighted by how close each question sits to a buying decision (core 1.0, adjacent 0.6, broad 0.3, peripheral 0.0). It reports commercial visibility without the deliberately unwinnable calibration questions dragging the number down.
Commercial intent
A 0–100 weight per tracked question, assembled in layers and always labelled with where it came from: ad prices (percentile-ranked Google Ads competition), read from the question (an LLM’s coarse ordinal), or topic estimate (the fallback prior). It is a prioritisation signal, not a forecast.
Action impact
Realized lift after you mark an action done. Bluemoon snapshots a before/after mention rate for the targeted questions against a control cohort of your other topics, with confidence intervals and a significance test — and reports “not enough data” when the sample is too thin to support a claim.
Agent Edge Network
The edge middleware you install in your own app. It detects AI crawler requests, verifies their identity, and decides whether to serve the variant or pass through, based on your serve mode and path rules.
Verification levels
Every request is classified SIGNED > IP_VERIFIED > UA_ONLY. Only SIGNED and IP_VERIFIED (verified) requests are ever served the variant. UA_ONLY is treated as unverified and always gets the normal page.
Optimized artifacts
Pre-generated page variants: answer-first markdown plus
Article / FAQPage JSON-LD. Built offline by strict, no-fabrication extraction and cached, so serving them adds nothing to the request path.Citability score
A per-page 0–1 heuristic of how citable an artifact is — rewarding a clear title, an answer-first lead, chunkable headings, a freshness date, FAQ structuring, and sufficient content density.
Serve modes
Control rollout: off (disabled), shadow (serve the normal page but log what would have been served), canary:<pct> (deterministic percentage), or default (all verified answer-engine agents).
Agent Analytics
The dashboard showing verified hits by purpose class and verification level, total hits, served URL count, and the share of served URLs that were subsequently cited.
How often questions are asked
Every active tracked question is asked once a day, on each platform your plan covers. The day’s sweep starts at the daily scan time your workspace picks in Settings → Daily scan time (UTC), so you decide when the fresh answers land. The cadence is deliberate rather than a limitation. An answer engine’s answer to a stationary question does not change every few hours, so more frequent sweeps buy freshness nobody can perceive while multiplying cost. If you want a fresh read sooner — you have just published something, or you are validating a change — a workspace admin can trigger a scan manually with Run scan. The first answers appear within about 15 seconds, badged Preliminary: an instant answer from the platform’s own API, which the full collected answer replaces in place within about two minutes. Scores and the intent classification follow within about two minutes of each answer landing.What the numbers do and do not claim
The measurement half is built so that every figure can be traced back to a stored answer. Three consequences are worth knowing before you read a dashboard. Commercial intent does not predict visibility. Measured across production domains, commercial intent correlates with visibility overall, but once the topic tier is held constant the correlation collapses to near zero — most of the apparent relationship is the tier speaking through the score. Use it to decide what to work on. Do not use it to forecast where you will be mentioned. Question shape survives that same control, which is why it is the axis the product points you at. Not measured is not zero. A question needs at least three recorded answers of its own before it counts toward a question-shape rate, because a question polled once contributes exactly 0% or 100%. Answers that were recorded but not yet scored are excluded from both the numerator and the denominator — an unscored answer is not evidence of absence. Bluemoon measures mentions, not causes. It can tell you that best-of-list questions in your core topics almost never name you. It cannot tell you why, and it does not know whether you publish content for that question shape. Any causal reading is yours to make from the recorded answers, which are all linked from the dashboard.Verification levels explained
The drop-in middleware verifies by published IP ranges and, optionally, Web Bot Auth signatures. A user-agent string alone is never trusted — a scraper can spoof it — so an unverified token match tops out at UA_ONLY and never reaches the variant.A further tier, DNS_VERIFIED (reverse-DNS confirmation), exists in the core and is resolved engine-side and asynchronously — it is deliberately off the drop-in middleware’s hot path, which is IP-only plus optional signatures.
Which agents Bluemoon recognizes
AEN maintains a registry of bots, each tagged with a vendor and a purpose class. By default, only answer-engine agents with a retrieval or user-triggered purpose are served the variant.Answer-engine agents (served by default)
OpenAI (OAI-SearchBot, ChatGPT-User), Anthropic (Claude-SearchBot, Claude-User), Perplexity (PerplexityBot, Perplexity-User), and Mistral (MistralAI-User).
Training crawlers (recognized, not served by default)
OpenAI GPTBot, Anthropic ClaudeBot, Common Crawl CCBot, ByteDance Bytespider, Google-Extended, Amazonbot, and Meta-ExternalAgent.
Search-index bots (always pass through)
Googlebot, Bingbot, and Applebot are never branched. Google’s AI Overviews run on Googlebot and the search index, so branching them would cloak Search itself.
Purpose classes
Every recognized bot carries a purpose: TRAINING, RETRIEVAL, SEARCH_INDEX, USER_TRIGGERED, or UNKNOWN. Your serve config chooses which purposes are eligible for the variant.
How the platform fits together
The measurement half runs entirely on Bluemoon’s side. Nothing needs to be installed on your site for it to work.Ready to connect your site? Provision a domain in Agent Analytics to mint an edge token (
aen_...), then head to the Quick Start to install the middleware.