Geo Monitor

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          # see the questions, spend nothing
node scripts/run-geo.mjs                    # registry -> engine -> answers + detection
node scripts/geo-dictionary.mjs --min 2     # harvest who was actually named (DRAFT only)
node scripts/redetect.mjs                   # re-read stored answers with a new dictionary, free
node scripts/geo-opportunities.mjs          # three gap types, counted separately
node scripts/geo-ledger.mjs                 # merge into the persistent, idempotent ledger

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

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                 # you plus every competitor

The two answer different questions and neither replaces the other:

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.