Pendium
Asimov
Asimov
Visibility25
Vibe82
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
AI Visibility Score
25/100

Low

Sentiment Score
82/100
Score by Reach

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
25
adjacent
36
aspirational
0
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.

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

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.

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 reach level — from core brand queries to growth opportunities.

ChatGPTChatGPT
ClaudeClaude
GeminiGemini
AI OverviewsAI Overviews

Reputation

Brand recognition & direct queries

21
21
39
0

Core Topics

Product/service category queries

0
35
55
0

Growth Areas

Adjacent, aspirational & visionary

0
0
0
Competitive Landscape
Scale AI
24 mentions
Ego4D
13 mentions
EgoBody
12 mentions
UnrealEgo
11 mentions
Labelbox
8 mentions
Appen
8 mentions
Loading visibility matrix...
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

1

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.

2

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.

3

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.

ChatGPTChatGPTClaudeClaudeGeminiGeminiAI OverviewsAI 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
ChatGPTChatGPT
1.EgoBody
2.EgoVIP
3.xR-EgoPose
4.ECHP
5.Ego-Exo4D

+3 more

ClaudeClaude
1.UnrealEgo (UnrealEgo-RW)
2.UnrealEgo2
3.HOI4D
4.Nymeria
5.Ego4D

+1 more

GeminiGemini
1.UnrealEgo
2.JRDB-Pose3D
3.EgoBody
4.EventEgo3D
5.Ego4D

+11 more

AI OverviewsAI 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
ChatGPTChatGPT
1.Scale AI
2.Labelbox
3.Supervisely
4.Hugging Face Datasets
5.Roboflow

+3 more

ClaudeClaude
1.EgoDex
2.Ego4D
3.Acuity AI
4.MANUS
5.UMI

+1 more

GeminiGemini
1.Cortex AI
2.Nexdata
3.Objectways
4.LXT
5.Appen

+6 more

AI OverviewsAI 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
ChatGPTChatGPT
1.MuJoCo
2.NVIDIA (Isaac Gym, Isaac Sim)
3.RoboSuite
4.PyBullet
5.RLlib

+3 more

ClaudeClaude
1.DiffuseDrive
2.RL-CycleGAN
3.Scale AI (Physical AI Data Engine)
4.RH20T
5.Sensei

+1 more

GeminiGemini
1.MIT
AI OverviewsAI Overviews
1.GenAug
2.ROSIE
3.RoboEngine
4.Open X-Embodiment
5.RT-X

+4 more

Competitive Landscape

Competitive Landscape

Brands and products that AI platforms mention alongside or instead of Asimov.

1Scale AI24 mentions
2Ego4D13 mentions
3EgoBody12 mentions
4UnrealEgo11 mentions
5Labelbox8 mentions
6Appen8 mentions
7Figure AI8 mentions
8Asimov7 mentions
9EgoDex6 mentions
10Open X-Embodiment6 mentions
11Slack6 mentions
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

Backing

Investors

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.

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