| name | geo-monitor |
| description | Run and read the GEO pipeline, which measures whether AI answer engines mention, recommend and cite your site. Puts a fixed registry of real user questions to an answer engine through the Monid tool layer, detects the three signals plus competitors, and turns "a rival is named and we are not" into a tracked work queue. Optionally cross checks against citation counts for eight assistants. Use when asked to run a GEO cycle, check AI answer visibility, find attack words, add questions to the registry, review the competitor dictionary, or interpret a mention/recommendation/citation rate. Triggers "GEO", "AI visibility", "attack words", "run a GEO cycle", "does ChatGPT mention us", "geo-monitor", "question registry", "AEO". |
GEO monitor
Sibling to seo-intake, same repo, same demand gate.
The reason this pipeline exists: losing in an AI answer is invisible. In
search, a lost customer still leaves an impression with no click. In an answer
they leave nothing at all: no click, no referrer, no log line. The only way to
know it happened is to ask the questions yourself and write down what came
back.
The cycle
node scripts/run-geo.mjs --dry-run
node scripts/run-geo.mjs
node scripts/geo-dictionary.mjs --min 2
node scripts/redetect.mjs
node scripts/geo-opportunities.mjs
node scripts/geo-ledger.mjs
run-geo takes --limit N and --cohort <name>. It is single threaded with a
gap between calls and a backoff on failure, because a throttled row and a
genuine absence look identical once they are written down, and one of them is
a lie. Failures are recorded as errors and excluded from every denominator.
The three signals, and which one you can actually move
| Signal | How it is read | How fast content moves it |
|---|
mentioned | your brand name in the answer text | slow, depends on what the model knows |
recommended | heuristic, see below | slower still |
cited | your domain in the answer's sources | fastest: this is what a good page changes |
cited is the GEO equivalent of a backlink and the one thing publishing
directly affects. It is also why a page that renders empty to a crawler hurts
more here than in search: an unreadable page can never enter a citation list at
all.
Never mix the cohorts
Brand defence questions name the brand almost by construction. Averaged with
generic questions they produce a healthy looking number that describes nothing
that happened.
Report the two lines separately, always, with denominators. The shape you
should expect on a young domain is a brand defence cohort at or near 100% and a
generic cohort at zero. That is not a bad measurement, that is the finding: the
engine knows you perfectly well when named, and never brings you up otherwise.
The recommendation detector is a heuristic and it has been wrong before
The generous version of this detector, the one that counts a bare "use" or
"try" as endorsement, fires on ordinary description: "a pay per use tool",
"how to use it", "works with Python". Every one of those reads as a
recommendation to a naive matcher and none of them is one.
The version here uses a short cue list and a narrow window, and it still
overcounts. Before reporting any non zero recommendation rate, read the
excerpts in recommendationEvidence and confirm them by hand. A sentence
like "the strongest signal is that it has an active product site" describes
evidence of legitimacy, not endorsement, and only a person can see that.
mentioned and cited are deterministic and need no such care.
The competitor dictionary cannot be guessed
The rivals a keyword tool names and the products an AI answer names are
different populations. Expect names that appear nowhere in your keyword gap to
show up in answer after answer, and expect some of your keyword rivals never to
be mentioned at all.
So: run first, harvest what was named, have a person confirm, then freeze and
version it. geo-dictionary.mjs only ever writes a .draft.json; promoting it
to competitor-dictionary.json is a human decision.
The harvest threshold is a decision about what counts as competition, not a
tuning knob. Set it high and a pile of answers come back as "absent, and
nobody else was named either", which reads like an empty market and is usually
a short dictionary. geo-opportunities.mjs warns when that group is larger
than the attack group, for exactly this reason.
After any dictionary change, run redetect.mjs. It re-reads the stored answers
for free, which is the only honest way to compare two parsing decisions: the
input is held fixed by construction, so any change in the numbers is the change
you made and not a different day of the internet.
Three gap types, never one number
brand-absent-competitor-present a rival is named, you are not attack
mentioned-not-recommended you are named, they get the nod defend
uncited you are named, your page is not earn the citation
A fourth group is tracked and is deliberately not an opportunity: absent,
and no known competitor named either. Those answers usually point at an
official platform API or explain a process without naming vendors, so there is
no slot to take. Counting them as attack words dilutes the real signal with
work that cannot be done.
The mustJoin hosts on an uncited row are the closest thing to an acceptance
test this pipeline produces: those pages were good enough to be a source for
that answer, so to join them yours has to be at least as useful. Read one or
two before writing anything.
The ledger is the working record
geo-ledger.mjs merges each run into data/geo-ledger.json with two
guarantees, and the file is worthless without either:
- Idempotent. Keyed by question plus reason, so re-running never
duplicates. What accumulates is
firstSeenRun, lastSeenRun and runsSeen,
which is how you tell a standing loss from a bad afternoon.
- Human fields survive.
status, owner, targetUrl, contentId,
notes and verdict are never overwritten by a merge; only measured fields
refresh. Break this and the first re-run silently erases every editorial
decision in the file.
A gap that stops appearing is marked resolved, not deleted: "we were absent
here and now we are not" is the only outcome evidence this system produces.
Do not claim a published article caused it without more than the sequence.
Edit a row by hand through the helper, so the guarantee holds:
node scripts/geo-ledger.mjs --list open
node scripts/geo-ledger.mjs --set GEO014 --status in-progress --owner me
Comparability contract
A trend is only real when the same questions ran against the same engine under
the same parser. Every run records registryVersion, detectorVersion,
competitorDictionaryVersion, the engine and the country. If any of those
change, start a new baseline rather than continuing the series.
Keep the fixed core fixed. Explore with new rows, not by rewording old ones.
The registry, and where questions come from
data/geo-registry.json. The chain is:
a real search term -> used as the search query on Reddit -> the real human
phrasing -> registry row
Not: a term reworded into a question by you. That step is lossy and it goes
wrong in a specific way, by reading a term in your own vocabulary instead of
the asker's. "bounce rate api" looks like phone validation and the real thread
is asking for an email lookup API to reduce bounce rates, which is a
deliverability problem with a different buyer entirely. Same words, different
market.
node scripts/questions-for.mjs "<term>" does the Reddit half for you.
sourceType records the distance travelled: exact (the asker's own words),
naturalized (your rewording), synthesized (your framing over evidenced
pain). Prefer exact, and keep the ratio honest: a registry that is mostly
synthesized is measuring your imagination.
evidenceGrade is confidence that people ask it, not commercial value.
That is what priority is for, which is why grade C plus P0 is a valid row.
One surface is not the market
run-geo probes one answer engine. That is not every engine, and an engine's
API is not always the same retrieval as its consumer product. Phrase every
finding with the surface named.
For breadth, ai-visibility.mjs counts how often eight assistants cite you,
including ChatGPT, Gemini, Copilot, Grok, Perplexity and Google AI Overviews:
node scripts/ai-visibility.mjs --dry-run
node scripts/ai-visibility.mjs
The two answer different questions and neither replaces the other:
| run-geo | ai-visibility |
|---|
| questions | yours, fixed, repeatable | somebody else's crawl |
| surfaces | one | eight |
| output | the full answers, stored | a count |
| tells you why you lost | yes, read the answer | no |
| cost | cents per question | dearer, one call per domain |
Use the count for share of answer against rivals and to catch the case where
you look fine on the one engine you probe and are invisible everywhere else.
Use the stored answers to learn what to fix. A count is a scoreboard, not a
diagnosis.
What comes next
node scripts/build-content-library.mjs fuses this with the search side. A
topic flagged on both surfaces is the strongest target available.node scripts/prospects-from-geo.mjs turns the citation list into ranked
backlink prospects, which is what backlink-outreach runs on.
