What does "AI visibility" mean for my business?

When someone asks ChatGPT, Claude, or Gemini "what is the best..." or "where can I find...", AI gives specific recommendations. AI visibility is a measure of how often — and how positively — these AI platforms mention your business. It's the new version of being on the first page of Google, except now there are no pages — just one answer.

Which AI platforms do you monitor?

Pendium monitors ChatGPT, Claude, Gemini, Grok, Perplexity, DeepSeek, and Google AI Overviews — seven major AI platforms that consumers use to research local services. We run real queries that your customers actually ask and analyze the responses for mentions of your business, competitors, and industry topics.

How does Pendium improve my visibility?

Three ways. First, we identify the exact queries and topics where you're invisible. Second, we generate AI-optimized content — articles, guides, social posts — designed to help AI agents understand your business and recommend your services to the right people. Third, we continuously monitor to track improvement and find new opportunities. Most clients see measurable improvement within 60 days.

How long until I see results?

You'll get your first visibility report within minutes of signing up. Actual visibility improvement varies, but our clients typically see a 47% average improvement within 60 days. Some see results faster — it depends on your current online presence and how competitive your local market is.

Do I need to be technical to use this?

Not at all. Enter your website URL and Pendium handles everything: scanning AI platforms, analyzing your visibility, generating content, and tracking improvement. The dashboard is designed for business owners, not engineers. If you can check your email, you can use Pendium.

What makes this different from SEO?

Traditional SEO optimizes for Google's search rankings — blue links on a results page. AI visibility is different: AI doesn't show links, it gives direct answers. Even if you rank #1 on Google, ChatGPT might recommend a competitor. Pendium optimizes for the new paradigm — making sure AI platforms know about, trust, and recommend your business.

Pricing

Get a demo

Asimov

Visibility20

Vibe82

Businesses/Artificial Intelligence & Robotics/Asimov

AI Visibility & Sentiment

Asimov

Asimov collects diverse real-world human activity data at scale to power the next generation of robotics AI. They capture egocentric video from everyday tasks across multiple continents, transforming it into clean, annotated datasets that teach robots to understand physical world interactions.

Active Monitoring

tryasimov.ai

Artificial Intelligence & Robotics YC25-26

AI Visibility Score

20/100

Low

Sentiment Score

82/100

Score by Priority

How often this business is recommended to users across different types of conversations — from direct product queries to broader open-ended conversations where AI could recommend this company's products and services

core

adjacent

aspirational

AI Perception

Key Takeaways

How AI platforms collectively perceive and describe Asimov today.

Asimov is successfully capturing top-tier visibility when AI models are asked about data solutions for embodied AI, particularly within Gemini where the brand frequently secures the number one position. However, this authority is inconsistent, as the brand remains largely invisible to key research personas and fails to capitalize on foundational industry queries that are currently dominated by competitors like Scale AI and Ego4D.

Working in your favor

Securing #1 rankings in Gemini for queries related to embodied AI training data and solving data scarcity for robotics.

High brand resonance with the 'Robotics Startup CTO' persona, achieving a 44% mention rate.

Strong performance in 'Adjacent' category queries, indicating a clear association with high-level AI research topics.

Gaps to close

Total absence from AI Overviews, creating a critical blind spot in modern search discovery.

Failure to engage 'Foundation Model Research Leads' and 'University Lab PIs' in foundational discussions.

Underperformance in 'Core' queries regarding data collection infrastructure and large-scale egocentric video datasets, where competitors dominate.

Opportunities

Expanding brand footprint into AI Overviews to capture early-funnel research queries.

Developing content specifically tailored to the technical requirements of university researchers and lab PIs to close the persona gap.

Optimizing technical content around 'data collection tech stacks' to convert the interest of robotics CTOs into broader market leadership.

Highest-Impact Actions

Optimize technical documentation for AI Overviews.

The current 0% mention rate in AI Overviews significantly hampers discovery for users performing initial research, which is a primary channel for competitor growth.

Develop and distribute white papers targeting 'University Lab PIs' and 'Foundation Model Research Leads'.

The total lack of visibility among these personas represents a massive untapped market segment that relies on peer-reviewed and research-heavy documentation.

Create authoritative content bridging the gap between 'data collection infrastructure' and 'embodied AI training'.

Competitors like Scale AI are winning on these queries; Asimov must demonstrate technical leadership in the 'how-to' space to displace incumbents.

Value Proposition

Solving robotics' data scarcity problem by collecting massive-scale, diverse human activity data from real environments across continents—not controlled labs—enabling robots to learn from the messy reality of how humans actually move through the physical world.

Overview

Mission

To bridge the massive gap between available robotics training data and the 16 hours of real-world physical activity generated by 8 billion people daily, enabling robots to truly understand the physical world.

Products & Services

Annotated robotics training datasetsEnd-to-end data collection pipelineEgocentric video capture with 3D body pose and depth mapsData collection hardware distributionPaid data collection opportunities for individuals

Current State

Visibility Landscape

A high-level view of how Asimov performs across AI platforms, broken down by strategic priority level — from core brand queries to growth opportunities.

	ChatGPT	Claude	Gemini	AI Overviews
Reputation1q Brand recognition & direct queries	94	70	70	70
“What do you know about Asimov? What do they do and what's their reputation?”	#2	Yes	Yes	Yes
Core3q Product/service category queries	35	35	68	0
“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”	#6	#6	#1	No
“help me build a tech stack for a robotics data collection pipeline”	No	No	#5	No
“most trusted robotics training data providers for frontier ai models”	No	No	No	No
Growth Areas3q Adjacent, aspirational & visionary	0	44	74	0
“how can i get paid to record myself doing chores for ai research”	No	No	No	—
“best ways to get diverse human activity data for embodied ai without using a lab”	No	#3	#1	No
“how to solve the data scarcity problem for general purpose robotics training”	No	No	#1	No

“What do you know about Asimov? What do they do and what's their reputation?”

Yes

“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”

“help me build a tech stack for a robotics data collection pipeline”

“most trusted robotics training data providers for frontier ai models”

“how can i get paid to record myself doing chores for ai research”

—

“best ways to get diverse human activity data for embodied ai without using a lab”

“how to solve the data scarcity problem for general purpose robotics training”

Competitive Landscape

Scale AI

24 mentions

Ego4D

13 mentions

EgoBody

12 mentions

UnrealEgo

11 mentions

Labelbox

8 mentions

Appen

8 mentions

Figure AI

8 mentions

Asimov

7 mentions

EgoDex

6 mentions

Open X-Embodiment

6 mentions

Slack

6 mentions

Analysis

Insights & Recommended Actions

What's working, what's not, and specific steps to improve Asimov's AI visibility.

Key Findings

Strength

Securing #1 rankings in Gemini for queries related to embodied AI training data and solving data scarcity for robotics.

Strength

High brand resonance with the 'Robotics Startup CTO' persona, achieving a 44% mention rate.

Strength

Strong performance in 'Adjacent' category queries, indicating a clear association with high-level AI research topics.

Recommended Actions

Optimize technical documentation for AI Overviews.

The current 0% mention rate in AI Overviews significantly hampers discovery for users performing initial research, which is a primary channel for competitor growth.

Develop and distribute white papers targeting 'University Lab PIs' and 'Foundation Model Research Leads'.

The total lack of visibility among these personas represents a massive untapped market segment that relies on peer-reviewed and research-heavy documentation.

Create authoritative content bridging the gap between 'data collection infrastructure' and 'embodied AI training'.

Competitors like Scale AI are winning on these queries; Asimov must demonstrate technical leadership in the 'how-to' space to displace incumbents.

Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

Training Embodied AI For Real World Tasks(3 queries)

“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”

0/4 platforms mentioned

Core

ChatGPT

1.EgoBody

2.EgoVIP

3.xR-EgoPose

4.ECHP

5.Ego-Exo4D

+3 more

Claude

1.UnrealEgo (UnrealEgo-RW)

2.UnrealEgo2

3.HOI4D

4.Nymeria

5.Ego4D

+1 more

Gemini

1.UnrealEgo

2.JRDB-Pose3D

3.EgoBody

4.EventEgo3D

5.Ego4D

+11 more

AI Overviews

1.Ego4D

2.Ego-Exo4D

3.Nymeria

4.EgoBody

5.Meta AI (Meta Open Source, Aria)

+4 more

“best ways to get diverse human activity data for embodied ai without using a lab”

0/4 platforms mentioned

Adjacent

The Foundation Model Research Lead · Senior Principal Scientist

ChatGPT

1.Scale AI

2.Labelbox

3.Supervisely

4.Hugging Face Datasets

5.Roboflow

+3 more

Claude

1.EgoDex

2.Ego4D

3.Acuity AI

4.MANUS

5.UMI

+1 more

Gemini

1.Cortex AI

2.Nexdata

3.Objectways

4.LXT

5.Appen

+6 more

AI Overviews

1.SO Development

2.Sapien

3.Open X-Embodiment

4.DROID

5.BridgeData V2

+5 more

“how to solve the data scarcity problem for general purpose robotics training”

0/4 platforms mentioned

Adjacent

The Foundation Model Research Lead · Senior Principal Scientist

ChatGPT

1.MuJoCo

2.NVIDIA (Isaac Gym, Isaac Sim)

3.RoboSuite

4.PyBullet

5.RLlib

+3 more

Claude

1.DiffuseDrive

2.RL-CycleGAN

3.Scale AI (Physical AI Data Engine)

4.RH20T

5.Sensei

+1 more

Gemini

1.MIT

AI Overviews

1.GenAug

2.ROSIE

3.RoboEngine

4.Open X-Embodiment

5.RT-X

+4 more

Source Intelligence

Citations

The sources AI platforms cite when recommending this brand. Pendium reverse-engineers what's already proven to be catnip to AI agents, then engineers content that fills gaps and helps agents do their job — which means more citations for you.

EgoBody

github.com

Code1 ref

Egovip

sites.google.com

Web1 ref

XR EgoPose

github.com

Code1 ref

EgoCentric Human Pose ECHP Dataset

github.com

Code1 ref

2311.18259

arxiv.org

Web1 ref

Index

hd-epic.github.io

Web1 ref

EMHI

pico-ai-team.github.io

Web1 ref

nlabel.ai

Web1 ref

GitHub

github.com

Code1 ref

EgoControl: Controllable Egocentric Video Generation via 3D Full-Body Poses

arxiv.org

Web1 ref

Ego-Body Pose Estimation via Ego-Head Pose Estimation

lijiaman.github.io

Web1 ref

Enhancing egocentric 3D pose estimation with third person views - ScienceDirect

sciencedirect.com

Web1 ref

Event-based Egocentric Human Pose Estimation in Dynamic Environment

arxiv.org

Web1 ref

3D Human Pose Perception from Egocentric Stereo Videos Hiroyasu Akada Jian Wang

openaccess.thecvf.com

Web1 ref

GitHub - Sid2697/awesome-egocentric-vision: A curated list of egocentric (first-person) vision and related area resources

github.com

Code1 ref

Brand Identity

Brand Voice & Style

How AI perceives Asimov's communication style and personality

Asimov communicates with technical precision balanced by accessible explanations, positioning themselves as serious problem-solvers tackling a fundamental challenge in robotics. Their voice is direct and confident, backed by data-driven arguments about the scale gap in robotics training data. They maintain a startup energy—ambitious yet practical—while speaking to both technical audiences and everyday people who might contribute data. The tone is professional but not corporate, with a clear mission-driven purpose.

Core Tone Traits

Technical yet Accessible

Explains complex robotics and AI concepts in clear, understandable terms without dumbing down the science

Mission-Driven & Purposeful

Communicates with conviction about solving a fundamental problem in robotics AI development

Direct & Confident

Makes bold claims backed by concrete data points and clear value propositions

Practical & Solutions-Oriented

Focuses on tangible outcomes and real-world applications rather than hype

Visual Identity

Primary

#000000

Secondary

#FFFFFF

Accent

#00D4D4

Background

#FFFFFF

Foreground

#111111

Backing

Investors

Y Combinator

Engineer content that makes AI agents recommend you

Pendium analyzes how AI platforms perceive your brand, reverse-engineers what they already cite, and continuously publishes content designed to fill gaps and earn more mentions — on autopilot, with you in the loop.

Data generated by Pendium.ai AI visibility scanning. Last scanned March 27, 2026.

Explore Artificial Intelligence & Robotics

View all

PrismaX AI

28/100

Start getting
recommended by AI.

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Frequently asked questions

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Businesses/Artificial Intelligence & Robotics/Asimov

AI Visibility & Sentiment

Asimov

Active Monitoring

tryasimov.ai

Artificial Intelligence & Robotics YC25-26

AI Visibility Score

20/100

Low

Sentiment Score

82/100

Score by Priority

core

adjacent

aspirational

AI Perception

Key Takeaways

How AI platforms collectively perceive and describe Asimov today.

Working in your favor

Securing #1 rankings in Gemini for queries related to embodied AI training data and solving data scarcity for robotics.

High brand resonance with the 'Robotics Startup CTO' persona, achieving a 44% mention rate.

Strong performance in 'Adjacent' category queries, indicating a clear association with high-level AI research topics.

Gaps to close

Total absence from AI Overviews, creating a critical blind spot in modern search discovery.

Failure to engage 'Foundation Model Research Leads' and 'University Lab PIs' in foundational discussions.

Underperformance in 'Core' queries regarding data collection infrastructure and large-scale egocentric video datasets, where competitors dominate.

Opportunities

Expanding brand footprint into AI Overviews to capture early-funnel research queries.

Developing content specifically tailored to the technical requirements of university researchers and lab PIs to close the persona gap.

Optimizing technical content around 'data collection tech stacks' to convert the interest of robotics CTOs into broader market leadership.

Highest-Impact Actions

Optimize technical documentation for AI Overviews.

The current 0% mention rate in AI Overviews significantly hampers discovery for users performing initial research, which is a primary channel for competitor growth.

Develop and distribute white papers targeting 'University Lab PIs' and 'Foundation Model Research Leads'.

The total lack of visibility among these personas represents a massive untapped market segment that relies on peer-reviewed and research-heavy documentation.

Create authoritative content bridging the gap between 'data collection infrastructure' and 'embodied AI training'.

Competitors like Scale AI are winning on these queries; Asimov must demonstrate technical leadership in the 'how-to' space to displace incumbents.

Value Proposition

Overview

Mission

Products & Services

Current State

Visibility Landscape

A high-level view of how Asimov performs across AI platforms, broken down by strategic priority level — from core brand queries to growth opportunities.

	ChatGPT	Claude	Gemini	AI Overviews
Reputation1q Brand recognition & direct queries	94	70	70	70
“What do you know about Asimov? What do they do and what's their reputation?”	#2	Yes	Yes	Yes
Core3q Product/service category queries	35	35	68	0
“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”	#6	#6	#1	No
“help me build a tech stack for a robotics data collection pipeline”	No	No	#5	No
“most trusted robotics training data providers for frontier ai models”	No	No	No	No
Growth Areas3q Adjacent, aspirational & visionary	0	44	74	0
“how can i get paid to record myself doing chores for ai research”	No	No	No	—
“best ways to get diverse human activity data for embodied ai without using a lab”	No	#3	#1	No
“how to solve the data scarcity problem for general purpose robotics training”	No	No	#1	No

“What do you know about Asimov? What do they do and what's their reputation?”

Yes

“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”

“help me build a tech stack for a robotics data collection pipeline”

“most trusted robotics training data providers for frontier ai models”

“how can i get paid to record myself doing chores for ai research”

—

“best ways to get diverse human activity data for embodied ai without using a lab”

“how to solve the data scarcity problem for general purpose robotics training”

Competitive Landscape

Scale AI

24 mentions

Ego4D

13 mentions

EgoBody

12 mentions

UnrealEgo

11 mentions

Labelbox

8 mentions

Appen

8 mentions

Figure AI

8 mentions

Asimov

7 mentions

EgoDex

6 mentions

Open X-Embodiment

6 mentions

Slack

6 mentions

Analysis

Insights & Recommended Actions

What's working, what's not, and specific steps to improve Asimov's AI visibility.

Key Findings

Strength

Securing #1 rankings in Gemini for queries related to embodied AI training data and solving data scarcity for robotics.

Strength

High brand resonance with the 'Robotics Startup CTO' persona, achieving a 44% mention rate.

Strength

Strong performance in 'Adjacent' category queries, indicating a clear association with high-level AI research topics.

Recommended Actions

Optimize technical documentation for AI Overviews.

The current 0% mention rate in AI Overviews significantly hampers discovery for users performing initial research, which is a primary channel for competitor growth.

Develop and distribute white papers targeting 'University Lab PIs' and 'Foundation Model Research Leads'.

The total lack of visibility among these personas represents a massive untapped market segment that relies on peer-reviewed and research-heavy documentation.

Create authoritative content bridging the gap between 'data collection infrastructure' and 'embodied AI training'.

Competitors like Scale AI are winning on these queries; Asimov must demonstrate technical leadership in the 'how-to' space to displace incumbents.

Programmatic Testing

Sample Conversations

ChatGPT

Claude

Gemini

AI Overviews

Training Embodied AI For Real World Tasks(3 queries)

“where can i find large scale egocentric video datasets with 3D body pose for training humanoid robots”

0/4 platforms mentioned

Core

ChatGPT

1.EgoBody

2.EgoVIP

3.xR-EgoPose

4.ECHP

5.Ego-Exo4D

+3 more

Claude

1.UnrealEgo (UnrealEgo-RW)

2.UnrealEgo2

3.HOI4D

4.Nymeria

5.Ego4D

+1 more

Gemini

1.UnrealEgo

2.JRDB-Pose3D

3.EgoBody

4.EventEgo3D

5.Ego4D

+11 more

AI Overviews

1.Ego4D

2.Ego-Exo4D

3.Nymeria

4.EgoBody

5.Meta AI (Meta Open Source, Aria)

+4 more

“best ways to get diverse human activity data for embodied ai without using a lab”

0/4 platforms mentioned

Adjacent

The Foundation Model Research Lead · Senior Principal Scientist

ChatGPT

1.Scale AI

2.Labelbox

3.Supervisely

4.Hugging Face Datasets

5.Roboflow

+3 more

Claude

1.EgoDex

2.Ego4D

3.Acuity AI

4.MANUS

5.UMI

+1 more

Gemini

1.Cortex AI

2.Nexdata

3.Objectways

4.LXT

5.Appen

+6 more

AI Overviews

1.SO Development

2.Sapien

3.Open X-Embodiment

4.DROID

5.BridgeData V2

+5 more

“how to solve the data scarcity problem for general purpose robotics training”

0/4 platforms mentioned

Adjacent

The Foundation Model Research Lead · Senior Principal Scientist

ChatGPT

1.MuJoCo

2.NVIDIA (Isaac Gym, Isaac Sim)

3.RoboSuite

4.PyBullet

5.RLlib

+3 more

Claude

1.DiffuseDrive

2.RL-CycleGAN

3.Scale AI (Physical AI Data Engine)

4.RH20T

5.Sensei

+1 more

Gemini

1.MIT

AI Overviews

1.GenAug

2.ROSIE

3.RoboEngine

4.Open X-Embodiment

5.RT-X

+4 more

Source Intelligence

Citations

EgoBody

github.com

Code1 ref

Egovip

sites.google.com

Web1 ref

XR EgoPose

github.com

Code1 ref

EgoCentric Human Pose ECHP Dataset

github.com

Code1 ref

2311.18259

arxiv.org

Web1 ref

Index

hd-epic.github.io

Web1 ref

EMHI

pico-ai-team.github.io

Web1 ref

nlabel.ai

Web1 ref

GitHub

github.com

Code1 ref

EgoControl: Controllable Egocentric Video Generation via 3D Full-Body Poses

arxiv.org

Web1 ref

Ego-Body Pose Estimation via Ego-Head Pose Estimation

lijiaman.github.io

Web1 ref

Enhancing egocentric 3D pose estimation with third person views - ScienceDirect

sciencedirect.com

Web1 ref

Event-based Egocentric Human Pose Estimation in Dynamic Environment

arxiv.org

Web1 ref

3D Human Pose Perception from Egocentric Stereo Videos Hiroyasu Akada Jian Wang

openaccess.thecvf.com

Web1 ref

GitHub - Sid2697/awesome-egocentric-vision: A curated list of egocentric (first-person) vision and related area resources

github.com

Code1 ref

Brand Identity

Brand Voice & Style

How AI perceives Asimov's communication style and personality

Core Tone Traits

Technical yet Accessible

Explains complex robotics and AI concepts in clear, understandable terms without dumbing down the science

Mission-Driven & Purposeful

Communicates with conviction about solving a fundamental problem in robotics AI development

Direct & Confident

Makes bold claims backed by concrete data points and clear value propositions

Practical & Solutions-Oriented

Focuses on tangible outcomes and real-world applications rather than hype

Visual Identity

Primary

#000000

Secondary

#FFFFFF

Accent

#00D4D4

Background

#FFFFFF

Foreground

#111111

Backing

Investors

Y Combinator

Engineer content that makes AI agents recommend you

Data generated by Pendium.ai AI visibility scanning. Last scanned March 27, 2026.

Explore Artificial Intelligence & Robotics

View all

PrismaX AI

28/100

Asimov

Key Takeaways

Visibility Landscape

Insights & Recommended Actions

Sample Conversations

Citations

Brand Voice & Style

Investors

Engineer content that makes AI agents recommend you

Explore Artificial Intelligence & Robotics

Start gettingrecommended by AI.

Frequently asked questions

Asimov

Key Takeaways

Visibility Landscape

Insights & Recommended Actions

Sample Conversations

Citations

Brand Voice & Style

Investors

Engineer content that makes AI agents recommend you

Explore Artificial Intelligence & Robotics

Start gettingrecommended by AI.

Frequently asked questions

Start getting
recommended by AI.

Start getting
recommended by AI.