Machine Relations Index

AI Visibility & GEO · Machine Relations Index category

Which sources AI engines cite for AI Visibility & GEO questions

Vendor-owned sources earn 52.9% of classified citations in AI Visibility & GEO answers (1,870 of 3,534), as of November 2, 2026.

In the November 2026 release of the Machine Relations Index, these are the sources AI answer engines cited most often for each of the 6 AI Visibility & GEO question shapes, each ranked on that shape's own observed runs. This category has 6 observed question-shape segments in the current public release. Each segment keeps its own observed-run denominator and dates; this page does not average rates across shapes. Observed runs are answer runs, not questions: every segment repeats a small set of distinct questions across engines and dates, so a run count is not a count of independent questions.

Top sources by question shape

The sources engines cite most, shape by shape

A rate is the share of the shape's observed answer runs that cited the source; rank is the source's place on that shape's full leaderboard.

Best tools

Example pattern: best AI visibility GEO tools. 284 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 616Otterly.aiOther observed source22.54%64 of 284
#2 of 616Seranking.comOther observed source22.54%64 of 284
#3 of 616RedditCommunity and social platform18.66%53 of 284
#4 of 616Hubspot.comVendor-owned source17.96%51 of 284
#5 of 616YouTubeSearch or media platform16.55%47 of 284

Full Best tools leaderboard, 616 sources

How buyers choose

Example pattern: how to choose AI visibility GEO tools. 282 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 699YouTubeSearch or media platform12.06%34 of 282
#2 of 699Stackmatix.comOther observed source10.99%31 of 282
#3 of 699Llmpulse.aiOther observed source8.87%25 of 282
#4 of 699Hubspot.comVendor-owned source8.51%24 of 282
#5 of 699Tryprofound.comVendor-owned source8.16%23 of 282

Full How buyers choose leaderboard, 699 sources

Is it worth it

Example pattern: is AI visibility GEO software worth it. 307 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 708Hubspot.comVendor-owned source17.59%54 of 307
#2 of 708RedditCommunity and social platform16.94%52 of 307
#3 of 708Stackmatix.comOther observed source15.31%47 of 307
#4 of 708Averi.aiOther observed source13.36%41 of 307
#5 of 708Presenc.aiOther observed source9.77%30 of 307

Full Is it worth it leaderboard, 708 sources

Problem-first research

Example pattern: how to solve AI visibility GEO problems. 301 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 682YouTubeSearch or media platform15.61%47 of 301
#2 of 682RedditCommunity and social platform14.95%45 of 301
#3 of 682LinkedInCommunity and social platform13.95%42 of 301
#4 of 682Semrush.comVendor-owned source11.30%34 of 301
#5 of 682Trysight.aiOther observed source8.97%27 of 301

Full Problem-first research leaderboard, 682 sources

Top lists

Example pattern: top AI visibility GEO platforms. 235 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 837Tryprofound.comVendor-owned source18.30%43 of 235
#2 of 837YouTubeSearch or media platform17.02%40 of 235
#3 of 837Therankmasters.comVendor-owned source16.17%38 of 235
#4 of 837Semrush.comVendor-owned source14.89%35 of 235
#5 of 837Hubspot.comVendor-owned source14.47%34 of 235

Full Top lists leaderboard, 837 sources

Comparisons

Example pattern: AI visibility GEO platform A vs B. 301 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 585Hubspot.comVendor-owned source26.25%79 of 301
#2 of 585Semrush.comVendor-owned source15.28%46 of 301
#3 of 585RedditCommunity and social platform12.62%38 of 301
#4 of 585MediumCommunity and social platform11.30%34 of 301
#5 of 585LinkedInCommunity and social platform10.96%33 of 301

Full Comparisons leaderboard, 585 sources

Observed shapes6

Canonical segment URLs exist only for observed category/shape pairs.

Published6

Published segments met the release evidence floor.

Collecting0

Collecting segments expose sample progress, not mature leaderboards.

ReleaseNovember 2, 2026

Methodology v2.1.

Question shapes

Separate denominators by segment

Question shapeStateObserved runsDistinct questionsRun datesDistinct cited domainsCanonical segment
Best toolsPublished2847 (5 paraphrase groups)13616Open leaderboard
How buyers choosePublished2827 (5 paraphrase groups)13699Open leaderboard
Is it worth itPublished3077 (5 paraphrase groups)13708Open leaderboard
Problem-first researchPublished3017 (5 paraphrase groups)13682Open leaderboard
Top listsPublished2357 (5 paraphrase groups)13837Open leaderboard
ComparisonsPublished3017 (5 paraphrase groups)13585Open leaderboard

By answer engine

Whole-window rates on each engine

Each link ranks sources with a published AI Visibility & GEO segment by that source's citation rate on one engine across the whole window. It is not a rate inside AI Visibility & GEO alone. A collecting engine has no published ranking for this category.
Cite this
Citation
Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai. AI Visibility & GEO, observed Machine Relations Index category, 2026-05-10 to 2026-11-02. Release mri_score_v2.1+2026-11-02+f9aaeca0db15. https://machinerelations.ai/index/categories/ai-visibility-geo. CC BY 4.0.
APA
Parrott, J. (2026). AI Visibility & GEO in the Machine Relations Index (Version mri_score_v2.1+2026-11-02+f9aaeca0db15) [Data set]. Machine Relations Research. https://machinerelations.ai/index/categories/ai-visibility-geo https://doi.org/10.5281/zenodo.23652423
BibTeX
@misc{parrott2026mriaivisibilitygeo,
  author = {Parrott, Jaxon},
  title = {AI Visibility \& GEO in the Machine Relations Index},
  year = {2026},
  version = {mri\_score\_v2.1+2026-11-02+f9aaeca0db15},
  publisher = {Machine Relations Research},
  url = {https://machinerelations.ai/index/categories/ai-visibility-geo},
  doi = {10.5281/zenodo.23652423},
  license = {CC-BY-4.0},
  note = {Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai}
}

CITATION.cff

Release and boundary

No pooled category score

Published November 2, 2026. The observation window runs May 10, 2026 to November 2, 2026. Methodology v2.1.

Use the data
curl -fsSL "https://machinerelations.ai/data/machine-relations-index.json" -o machine-relations-index.json
curl -fsSL "https://machinerelations.ai/data/mri-release-manifest.json" -o mri-release-manifest.json
curl -fsSL "https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv" -o domains.csv
curl -fsSL -L "https://doi.org/10.5281/zenodo.23652423" -o mri-zenodo.html
import json
from urllib.request import urlopen

index_url = "https://machinerelations.ai/data/machine-relations-index.json"
with urlopen(index_url) as response:
    index = json.load(response)
print(index["generated"], index["total_domains"])

This page is a directory of observed segments. A domain's overall citation rate is not a category rate, and MRI does not publish an averaged category score in this slice.

AI Visibility & GEO buyers on AuthorityTech’s publication intelligence is a downstream practitioner projection of these segments. It is not source evidence, citation provenance, or an identity link for the neutral dataset.