Overview Leaderboard Methodology
Industry Report

Collaborative Document Editors

Real-time, cloud-based document editing platforms that enable multiple users to create, edit, and comment on text documents simultaneously.

Brands tracked: 29
Brands analyzed: 29
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 239
Total responses: 1,190
Top Brand Overall?
Google
100/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Google
100

Highest score on the LBA metric in this industry.

Authority Leader?
Google
100

Highest score on the Authority metric in this industry.

TOM Leader?
Google
100

Highest score on the TOM metric in this industry.

Google is the default answer in AI responses for Collaborative Document Editors

Google and Microsoft together dominate AI responses for Collaborative Document Editors. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 29 Collaborative Document Editors brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Google
google.com
 100
100
100
100
2
Microsoft
microsoft.com
 96
86
96
93
3
Notion
notion.so
 83
64
87
77
4
Dropbox
dropbox.com
 82
30
63
54
5
Coda
coda.io
 82
22
42
43
6
ONLYOFFICE
onlyoffice.com
 71
9
46
33
7
Zoho
zoho.com
 72
17
26
32
8
Quip
quip.com
 67
7
39
28
9
Atlassian
atlassian.com
 78
8
23
26
10
Etherpad
etherpad.org
 65
6
18
21
11
ClickUp
clickup.com
 79
8
8
19
12
Overleaf
overleaf.com
 80
8
8
19
13
HackMD
hackmd.io
 74
7
7
18
14
Craft
craft.do
 68
7
8
17
15
Feishu
feishu.cn
 71
7
7
17
16
Slite
slite.com
 70
7
7
17
17
XWiki
xwiki.com
 70
7
7
17
18
Lark
larksuite.com
 63
6
6
16
19
Nuclino
nuclino.com
 66
7
7
16
20
CryptPad
cryptpad.fr
 62
6
6
15
21
Tencent Docs
docs.qq.com
 60
6
6
15
22
Tettra
tettra.com
 61
6
6
15
23
WPS Docs
wps.com
 59
6
6
15
24
BookStack
bookstackapp.com
 54
5
5
14
25
Outline
getoutline.com
 56
6
6
14
26
Fibery
fibery.io
 50
5
5
13
27
Guru
getguru.com
 51
5
5
13
28
Document360
document360.com
 43
4
4
11
29
Hancom Docs
hancomdocs.com
 39
4
4
10

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 239 shared Collaborative Document Editors prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 239 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →