Roboflow

Visibility57

Vibe97

Businesses/Artificial Intelligence & Machine Learning/Roboflow

AI Visibility & Sentiment

Roboflow

Roboflow is a computer vision platform that enables developers and enterprises to build, train, and deploy AI models that can see and understand the physical world. Over 1 million engineers use Roboflow to deploy visual intelligence for video, images, and real-time streams across industries like manufacturing, logistics, and robotics.

Active Monitoring

roboflow.com

Artificial Intelligence & Machine Learning Startups

AI Visibility Score

57/100

Moderate

Sentiment Score

97/100

AI Perception

Summary

Roboflow has successfully positioned itself as the definitive authority for custom YOLO model training and startup computer vision projects, frequently securing the #1 recommendation spot across major LLMs. While the brand dominates the narrative for high-precision researchers, it faces a critical visibility vacuum in edge-deployment and hardware-specific queries where competitors currently control the conversation.

Value Proposition

End-to-end computer vision platform that takes developers from idea to deployed application, enabling them to train models in an afternoon and deploy to cloud or edge with minimal friction

Overview

Mission

To make the world programmable by empowering developers and enterprises to build their own fast, accurate, and scalable computer vision applications

Products & Services

Data labeling and annotation toolsModel training infrastructureCloud-hosted inference APIEdge deployment solutionsOpen source libraries (Supervision, Inference, Autodistill)

Agent Breakdown

AI Platforms

How often do different AI platforms reference Roboflow?

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Conversation Analysis

Topics

What conversations is Roboflow included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Roboflow to, and when?

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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

Building Custom Vision Applications(3 queries)

“how do I build a model to detect defects on a manufacturing line”

2/4 platforms mentioned

ChatGPT

1.Basler

2.FLIR

3.Point Grey

4.Allied Vision

5.AHS

18.Roboflow

+44 more

Claude

1.YOLOv8

2.Faster R-CNN

3.RetinaNet

4.ResNet

5.EfficientNet

10.Roboflow

+13 more

Gemini

1.Basler

2.HIKROBOT

3.Smart View

4.CCS Inc.

5.CVAT

+19 more

AI Overviews

1.eInfochips

2.YOLOv8

3.CVAT

4.Label Studio

5.PyTorch

+7 more

“what's the easiest way to train a custom YOLO model with my own images”

4/4 platforms mentioned

ChatGPT

1.Ultralytics YOLO

2.YOLO

3.Roboflow

4.CVAT

5.LabelImg

+26 more

Claude

1.Roboflow

2.YOLOv8

3.Ultralytics

4.Python

Gemini

1.AWS Rekognition

2.Ultralytics

3.Roboflow

4.YOLO

5.YOLOv8

+12 more

AI Overviews

1.YOLO

2.YOLO11

3.YOLOv8

4.Roboflow

5.Google Colab

+3 more

“help me set up a computer vision project to track inventory in a warehouse”

1/4 platforms mentioned

ChatGPT

1.NVIDIA Jetson

2.Intel NUC

3.NVIDIA

4.Kubernetes

5.Scale AI

+46 more

Claude

1.Intel RealSense

2.IDS cameras

3.NVIDIA Jetson

4.AWS Snowball Edge

5.AWS Rekognition

8.Roboflow

+4 more

Gemini

1.Rekognition

2.Axis Communications

3.Basler

4.NVIDIA Jetson AGX Orin

5.NVIDIA A100

+16 more

AI Overviews

1.Sony IMX183

2.NVIDIA Jetson Orin

3.inFlow Inventory

4.OpenCV

5.PyTorch

+6 more

Computer Vision Infrastructure & Deployment(1 query)

“how can i run model inference at the edge on a raspberry pi or jetson nano, what tools should i use”

0/4 platforms mentioned

ChatGPT

1.Raspberry Pi

2.TensorFlow Lite

3.Google Coral

4.Edge TPU

5.ONNX Runtime

+30 more

Claude

1.Raspberry Pi

2.Jetson Nano

3.TensorFlow Lite

4.ONNX Runtime

5.TVM

+14 more

Gemini

1.Raspberry Pi

2.NVIDIA Jetson Nano

3.NVIDIA

4.CUDA

5.NVIDIA TensorRT

20.Edge Impulse

+17 more

AI Overviews

1.Jetson Nano

2.Raspberry Pi

3.NVIDIA TensorRT

4.NVIDIA GPUs

5.PyTorch

+15 more

Trust & Category Evaluation(1 query)

“best computer vision platforms for startups that aren't just AWS or Google, what are the top choices”

4/4 platforms mentioned

ChatGPT

1.Roboflow

2.YOLO

3.TensorFlow

4.ONNX

5.Viso Suite

+35 more

Claude

1.Roboflow

2.Clarifai

3.Azure Custom Vision

4.Ultralytics

5.YOLOv8

+4 more

Gemini

1.AWS

2.Google Cloud

3.Roboflow

4.YOLOv8

5.PyTorch

+20 more

AI Overviews

1.AWS

2.Roboflow

3.Roboflow Universe

4.Clarifai

5.Scribe

+15 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Exceptional dominance in YOLO-related training queries, consistently ranking #1 to #5 across ChatGPT, Claude, and Gemini.

Strength

High authority with the Precision-Focused Medical Researcher persona, achieving a 75% mention rate with strong positive sentiment.

Strength

Primary recommendation for startups and new vision projects, outperforming established competitors in 'best platform' category evaluations.

Gap

Complete absence from edge-inference and hardware deployment queries, specifically regarding Raspberry Pi and NVIDIA Jetson integrations.

Gap

Significantly lower visibility on Google Gemini (33%) compared to Claude and ChatGPT (75%), indicating a missed opportunity in search-integrated AI.

Gap

Inconsistent sentiment on Claude, where high mention rates are offset by mixed technical critiques of the platform's infrastructure.

Opportunity

Capturing the hardware-adjacent market by aggressively documenting deployment workflows for TensorRT and edge devices to displace NVIDIA Jetson's narrative lead.

Opportunity

Leveraging high mention rates on ChatGPT to push more enterprise-focused features, moving beyond the 'startup' label.

Opportunity

Closing the Gemini visibility gap by optimizing technical documentation for Google's search-grounded AI models.

Technical Health

Site Health for AI Visibility

How well Roboflow's website is optimized for AI agent discovery and comprehension.

95/100

21 passed 2 warnings

Audited 2/28/2026

Crawlability100

Can AI bots find your pages?

Technical90

SSL, mobile, doctype basics

On-Page SEO100

Titles, descriptions, headings

Content Quality87

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Warnings

4 render-blocking resources are slowing initial render

Defer non-critical JS with async/defer. Inline critical CSS. Move stylesheets to load asynchronously.

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Brand Identity

Brand Voice & Style

How AI perceives Roboflow's communication style and personality

Roboflow communicates with a developer-first, technically credible voice that balances deep expertise with accessibility. The brand is confident and ambitious about the transformative potential of computer vision while remaining grounded in practical, real-world applications. Content emphasizes empowerment and speed-to-value, using clear technical language without unnecessary jargon. There's an underlying enthusiasm for open source and community contribution that creates an approachable, collaborative tone.

Core Tone Traits

Developer-Centric & Technical

Speaks the language of engineers with code examples, technical accuracy, and respect for developer workflows

Confident & Ambitious

Bold claims backed by evidence - 'AI that sees and understands the physical world' - positioning as industry leader

Practical & Results-Oriented

Focuses on real outcomes like 'idea to production today' and concrete customer metrics like '$8 million saved'

Open & Community-Driven

Emphasizes open source contributions, developer community, and collaborative approach to building

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Roboflow

YOLOv831 mentions

Roboflow28 mentions

PyTorch26 mentions

Ultralytics20 mentions

TensorRT19 mentions

TensorFlow17 mentions

NVIDIA Jetson16 mentions

Labelbox16 mentions

CVAT15 mentions

YOLO15 mentions

ONNX14 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

ultralytics.com

http://ultralytics.com/

Referenced in 1 query

Review

roboflow.com

http://roboflow.com/

Referenced in 1 query

Review

Building a Defect Detection Pipeline: The Simulator's Way

https://www.einfochips.com/blog/building-a-defect-detection-pipeline-the-simulators-way/

Referenced in 1 query

Review

Can I Train a Model to Detect Defects Using Only Good Images?

https://www.reddit.com/r/computervision/comments/1em7fgf/can_i_train_a_model_to_detect_defects_using_only/

Referenced in 1 query

Join Discussion

Image-Based Defect Detection Using Deep Learning in Manufacturing

https://medium.com/@rvtjmeda/image-based-defect-detection-using-deep-learning-in-manufacturing-0495067ba75f

Referenced in 1 query

Review

Democratize computer vision defect detection for manufacturing ...

https://aws.amazon.com/blogs/machine-learning/democratize-computer-vision-defect-detection-for-manufacturing-quality-using-no-code-machine-learning-with-amazon-sagemaker-canvas/

Referenced in 1 query

Partner

Defect Detection with Computer Vision – How Does it Work?

https://dac.digital/how-defect-detection-with-computer-vision-works/

Referenced in 1 query

Review

Defect Detection in Manufacturing: An Integrated Deep ...

https://www.scirp.org/journal/paperinformation?paperid=136828

Referenced in 1 query

Review

Defect Data Augmentation Method for Robust Image-based ...

https://papers.phmsociety.org/index.php/phme/article/download/4068/2410

Referenced in 1 query

Review

Figuring out which approach is best for defect detection in a ...

https://www.reddit.com/r/computervision/comments/1af8bww/figuring_out_which_approach_is_best_for_defect/

Referenced in 1 query

Join Discussion

How to Track and Prevent Manufacturing Quality Defects - Tulip

https://tulip.co/blog/manufacturing-quality-defect-tracking/

Referenced in 1 query

Review

Step-by-Step Guide to Preparing Data & Training AI Models ...

https://www.youtube.com/watch?v=031veMYrtRI

Referenced in 1 query

Pitch Story

Content Engineering

Recommended Actions

Develop and publish high-authority documentation focused on 'Edge Inference' and 'Raspberry Pi Deployment'.

Roboflow is currently invisible in computer vision infrastructure queries, allowing hardware competitors to capture users at the deployment phase.

Impact: High

Optimize technical content for Gemini's indexing to improve the 33% mention rate.

Gemini is the only major platform where the brand is significantly underrepresented, creating a massive blind spot for users in the Google ecosystem.

Impact: High

Launch a series of technical deep-dives into enterprise-grade logistics and robotics use cases.

Current visibility with logistics and robotics personas is only 50%, representing a significant growth area for high-value enterprise contracts.

Impact: Medium

Audit documentation cited by Claude to address the 'mixed sentiment' regarding platform performance.

Despite a high rank, the mixed sentiment suggests LLMs are picking up on user friction or limitations in the deployment workflow.

Impact: Medium

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Data generated by Pendium.ai AI visibility scanning. Last scanned February 28, 2026.

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