AI Scores / Tech & SaaS

Tech & SaaS AI Scores
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AI Visibility Scores for Tech and SaaS Brands

B2B buyers ask AI engines for software recommendations every day. The SaaS brands that show up have schema markup, entity clarity, and AI-accessible documentation. See how tech companies score on AI visibility.

3,722,635
Brands Scored
3.7/10
Industry Average
<1%
AI-Ready (8+)

Score Distribution - 3,722,635 Tech & SaaS Brands

AI-Ready8-10
<1%
1,948
Getting There6-7
7%
242,187
Needs Work4-5
53%
1,983,539
Not Discoverable0-3
40%
1,494,961

Only <1% of tech & saas brands are AI-ready.

The other >99% are being skipped by ChatGPT, Perplexity, and Google AI Mode.

Score your brand free

3,722,635 brands scored - page 57 of 37,227

Y

Yhzhtk Pages๏ผˆๆŠ€ๆœฏๅšๅฎข๏ผ‰

7.4/10
H

Hongtu Zang

7.4/10
G

Global Human Settlement

7.4/10
T

The GitLab Handbook

7.4/10
K

Koichi Namekata

7.4/10
H

Home

7.4/10
T

The Smithsonian's Human Origins Program

7.4/10
C

Cleveland Clinic

7.4/10
I

Inside Atlassian

7.4/10
G

Galaxy Community Hub

7.4/10
M

Migician

7.4/10
H

Home

7.4/10
A

Audit & Repair

7.4/10
E

Elsevier Developer PortalElsevier logo

7.4/10
A

Apache Foryโ„ข

7.4/10
P

Palantir

7.4/10
Z

Zunnan Xu

7.4/10
Z

Zhen Xiang

7.4/10
J

Jia Chen ๏ผˆ้™ˆไฝณ๏ผ‰

7.4/10
P

Physical Vision Group

7.4/10
G

GigaWorld

7.4/10
R

Raghavendra Kaushik

7.4/10
Y

Yuezhou Hu (่ƒก่ถŠ่ˆŸ)

7.4/10
H

Home

7.4/10
J

Junhong Chen

7.4/10
F

From Words to Structured Visuals: A Benchmark and Framework for Text

7.4/10
D

DialogCC: An Automated Pipeline for Creating High

7.4/10
Y

Yan Lu (้ฒ็‚Ž)

7.4/10
D

Do Language Models Understand Honorific Systems in Javanese?

7.4/10
L

LHL's Homepage

7.4/10
M

MT_Box

7.4/10
Y

Yifan Luo

7.4/10
V

Vijayabharathi Murugan

7.4/10
M

MLM

7.4/10
D

DeRยฒ: Retrieval

7.4/10
Y

Yiming Wang

7.4/10
Z

Zhaojiang Lin

7.4/10
R

Ruchit Rawal

7.4/10
N

Noah Frahm Homepage

7.4/10
O

Office of Research and Innovation at MSU

7.4/10
W

Why Are Web AI Agents More Vulnerable Than Standard LLMs?

7.4/10
H

HERO: Human

7.4/10
A

AbacusSummit โ€” AbacusSummit

7.4/10
Z

Zedong Wang

7.4/10
R

RobIn Robot Interactive Intelligence Lab

7.4/10
T

The 2nd Workshop on Human

7.4/10
M

MAP

7.4/10
M

Material Editing in CLIP Space

7.4/10
G

GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling

7.4/10
Y

Yijie Lin

7.4/10
A

Adaptive Caching for Faster Video Generation with Diffusion Transformers

7.4/10
M

Mobile Perception Systems Lab

7.4/10
D

Dr. Krishan Rana

7.4/10
D

Dosung Lee

7.4/10
R

Raj Ghugare

7.4/10
S

SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition

7.4/10
R

Ref

7.4/10
J

Jiahao Zhan

7.4/10
T

TetraGrip: Sensor

7.4/10
V

VividDream: Generating 3D Scene with Ambient Dynamics

7.4/10
J

Jiahe Li's Homepage

7.4/10
N

Nahyuk Lee

7.4/10
I

IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity

7.4/10
W

When and How Much to Imagine: Adaptive Test

7.4/10
D

Dual

7.4/10
G

GERNE: Gradient Extrapolation for Debiased Representation Learning

7.4/10
G

Gwangtak Bae

7.4/10
J

Junyang Chen (้™ˆไฟŠ้˜ณ)

7.4/10
L

llm-enhance

7.4/10
A

Anh Thai

7.4/10
Q

Qi Yan

7.4/10
D

Dr. Rafael S. de Souza

7.4/10
S

Spot

7.4/10
H

Hanyang Yu

7.4/10
L

Lutao Jiang

7.4/10
M

Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video

7.4/10
S

SignBERT+

7.4/10
B

blog.google

7.4/10
L

Latest News from Google Research Blog

7.4/10
C

Check Point Blog

7.4/10
H

Home

7.4/10
M

MetaCanvas

7.4/10
A

Agent Banana

7.4/10
T

Tejas' Blog

7.4/10
N

Natron

7.4/10
N

Northwestern University Knight Lab

7.4/10
B

bennylin.github.io

7.4/10
G

GitHub

7.4/10
F

Federal Register :: Request Access

7.4/10
R

Robert A. McNees

7.4/10
N

Nusa Apps

7.4/10
T

The Milo Project

7.4/10
6

6FF

7.4/10
S

Sumalya Studios

7.4/10
E

Esri Developer

7.4/10
U

Ubuntu font

7.4/10
C

C++ packet sniffing and crafting library

7.4/10
T

The State of Developer Ecosystem in 2025

7.4/10
M

merdarahta

7.4/10
A

Ahrefs for Developers

7.4/10
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Score your tech & saas brand free

Want to improve your AI visibility score?

Read: AEO guide for Tech & SaaS brands โ†’

What an AEO score of 3.7/10 means for tech & saas brands

The average Tech & SaaS brand we've scored sits at 3.7/10. That means roughly <1% are positioned to be cited by ChatGPT, Claude, and Perplexity, while the remaining >99% are at risk of being skipped when AI engines answer customer questions.

Common AEO issues in tech & saas

  • Missing Organization JSON-LD with sameAs links to social profiles
  • No llms.txt file declaring the brand to AI crawlers
  • FAQ content rendered without FAQPage schema, so AI engines cannot extract it
  • robots.txt or Cloudflare WAF blocking GPTBot, ClaudeBot, or PerplexityBot
  • Thin About pages with no founder/team Person schema

How to improve your tech & saas AEO score

Run a free AEO score for your site and see exactly which signals are missing. The audit takes under a minute and the fix list ships as a one-click JSON-LD + llms.txt + robots.txt patch you can paste into any platform.

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