Overview Leaderboard Methodology
Industry Report

Email Deliverability Tools

Tools and services that monitor, analyze, and improve inbox placement, sender reputation, and bounce/spam metrics for email senders.

Brands tracked: 37
Brands analyzed: 37
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 282
Total responses: 1,415
Top Brand Overall?
GlockApps
81/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Litmus
91

Highest score on the LBA metric.

Authority Leader?
GlockApps
78

Highest score on the Authority metric.

TOM Leader?
Validity
100

Highest score on the TOM metric.

GlockApps leads the Email Deliverability Tools category in AI responses

GlockApps is the most consistently-surfaced brand when users ask AI assistants about Email Deliverability Tools, but not to the point of monopolising the conversation. Validity and a handful of others appear regularly, giving users a reasonable set of alternatives to compare. The top of this leaderboard is where most of the AI-driven traffic will go.

Brand Leaderboard All 37 Email Deliverability Tools brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
GlockApps
glockapps.com
 73
78
92
81
2
Validity
validity.com
 79
46
100
72
3
Litmus
litmus.com
 91
21
50
46
4
Mailgun
mailgun.com
 84
21
43
42
5
Google
google.com
 71
28
32
40
6
Microsoft
microsoft.com
 87
19
34
38
7
Postmark
postmarkapp.com
 86
15
38
37
8
SendGrid
sendgrid.com
 88
15
35
36
9
Mail-Tester
mail-tester.com
 58
25
19
30
10
NeverBounce
neverbounce.com
 79
3
21
16
11
MailReach
mailreach.co
 32
6
17
15
12
ZeroBounce
zerobounce.net
 85
1
29
14
13
Folderly
folderly.com
 41
6
10
13
14
Lemwarm
lemlist.com
 63
2
17
12
15
dmarcian
dmarcian.com
 73
1
22
11
16
Kickbox
kickbox.com
 71
1
27
11
17
MailMonitor
mailmonitor.com
 13
2
25
9
18
SparkPost
sparkpost.com
 77
0
32
7
19
EasyDMARC
easydmarc.com
 74
0
14
2
20
Inbox Monster
inboxmonster.com
 36
0
15
2
21
InboxAlly
inboxally.com
 59
0
15
2
22
MXToolbox
mxtoolbox.com
 0
16
24
2
23
SendForensics
sendforensics.com
 36
0
29
2
24
Valimail
valimail.com
 78
0
18
2
25
Warmup Inbox
warmupinbox.com
 41
0
16
2
26
Bouncer
usebouncer.com
 70
0
5
1
27
Mailtrap
mailtrap.io
 76
0
1
1
28
Mailwarm
mailwarm.com
 29
0
3
1
29
PowerDMARC
powerdmarc.com
 72
0
4
1
30
Red Sift
redsift.com
 60
0
3
1
31
Spamhaus
spamhaus.org
 76
0
0
1
32
Allegrow
allegrow.co
 25
0
0
0
33
DeBounce
debounce.io
 53
0
0
0
34
eDataSource
edatasource.com
 36
0
0
0
35
Emailable
emailable.com
 50
0
0
0
36
Proofy
proofy.io
 14
0
0
0
37
URIports
uriports.com
 20
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 282 shared Email Deliverability Tools 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
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 282 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 →