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Geberit Quotes

After running pilots with several annotation providers, Label Your Data delivered the strongest results by a clear margin, standing out on turnaround time, annotation quality, and the responsiveness of their feedback loops.

Quotes
Geberit
Maxime Debarbat

Maxime Debarbat

Senior ML Engineer (GenAI)

Trusted by ML Professionals

Ouster
Searidge Technologies
Zendar
Advanced Farm
ABB
Toptal
UiPath
Respeecher
Yale
Thorvald

Data Annotation Services

Outsource data annotation to experts

RUN FREE PILOT

Trusted by 200+ Customers

ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX

Use Cases for

ML Engineers

ML Engineers

You are working on a supervised learning / RLHF project

You are working on a supervised learning / RLHF project

In-house annotations take too long

In-house annotations take too long

Dataset Business

Dataset Business

You are selling annotated datasets

You are selling annotated datasets

Need to process raw datasets

Need to process raw datasets

AI-Powered Business

AI-Powered Business

Your business is using an ML model to recognize objects

Your business is using an ML model to recognize objects

Your raw data stockpiles faster than you can process it

Your raw data stockpiles faster than you can process it

Academic Researchers

Academic Researchers

Peer reviewers require annotated datasets

Peer reviewers require annotated datasets

You can’t afford to spend time on annotations

You can’t afford to spend time on annotations

Discuss your challenges

Data Services for You

An image of a frog, used as an example of a rectangle-based image annotation
An image of a frog, used as an example of a polygon-based image annotation
An image of a frog, used as an example of a 3D cuboid-based image annotation
An image of a frog, used as an example of a keypoint-based image annotation
An image of a frog, used as an example of an instance segmentation annotation applied to an image
An image of a frog, used as an example of a semantic segmentation annotation applied to an image
An image of a frog, used as an example of a panoptic segmentation annotation applied to an image
placeholder
Bounding boxes Bounding boxes
Polygons Polygons
Cuboids Cuboids
Keypoints Keypoints
Instance segmentation Instance segmentation
Semantic segmentation Semantic segmentation
Panoptic segmentation Panoptic segmentation
Lidar / Radar Lidar / Radar
Pick the service you need
An example of text classification depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Each word is annotated as a part of speech like noun or verb
An example of named entity recognition depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Frodo is annotated as a name, the frog as an animal, and the tree as a location
An example of sentiment analysis depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The words determined and hopeful are annotated as positive, while the word slowly crawled is annotated as neutral
An example of sentence comparison depicted as an image of 2 sentences. The first sentence is: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The second sentence is: Sam the toad, feeling anxious and tired, climbed the tree in search of shelter
An example of audio to text transcription depicted as a sound wave like in the audio editor software. Some parts of the wave are annotated to show what’s being said
Text Classification Text Classification
Named Entity Recognition Named Entity Recognition
Sentiment Analysis Sentiment Analysis
Comparison Comparison
Audio-To-Text Transcription Audio-To-Text Transcription
Pick the service you need
An example of a data annotation edge case depicted as a bird next to a flower that looks like a bird. But the flower is wrongfully recognized as a bird with 92% accuracy
An example of an object recognition of a bird with 98% accuracy
Quality Assurance Quality Assurance
Model Validation Model Validation
Pick the service you need
An example of 2 LLMs generating different inferences for data annotator to choose the best response
An example of one LLM generating two different inferences for data annotator to choose the best one
An example of a user submitting a detailed prompt with a detailed response from the LLM
An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image
An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description
An image example of semantic segmentation with a title: train your LLM like Segment Anything to annotate objects on images
LLM Comparison LLM Comparison
Question-Answer Pairs Question-Answer Pairs
Prompts Generation Prompts Generation
Image and Text Alignment Image and Text Alignment
Image Captioning Image Captioning
Object Detection and Classification Object Detection and Classification
Pick the service you need

Request to gather publicly available or field data.

Pick the service you need
An example of content moderation: identified NSFW content on social media. It is censored and labeled as NSFW.

Get a dedicated content moderator or annotate your NSFW social data to train your model.

Pick the service you need

Beyond annotation, process your data with services like digitizing, parsing, and cleansing.

Pick the service you need
An image of a frog, used as an example of a rectangle-based image annotation
An image of a frog, used as an example of a polygon-based image annotation
An image of a frog, used as an example of a 3D cuboid-based image annotation
An image of a frog, used as an example of a keypoint-based image annotation
An image of a frog, used as an example of an instance segmentation annotation applied to an image
An image of a frog, used as an example of a semantic segmentation annotation applied to an image
An image of a frog, used as an example of a panoptic segmentation annotation applied to an image
placeholder
Bounding boxes Bounding boxes
Polygons Polygons
Cuboids Cuboids
Keypoints Keypoints
Instance segmentation Instance segmentation
Semantic segmentation Semantic segmentation
Panoptic segmentation Panoptic segmentation
Lidar / Radar Lidar / Radar
Pick the service you need
An example of text classification depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Each word is annotated as a part of speech like noun or verb
An example of named entity recognition depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Frodo is annotated as a name, the frog as an animal, and the tree as a location
An example of sentiment analysis depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The words determined and hopeful are annotated as positive, while the word slowly crawled is annotated as neutral
An example of sentence comparison depicted as an image of 2 sentences. The first sentence is: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The second sentence is: Sam the toad, feeling anxious and tired, climbed the tree in search of shelter
An example of audio to text transcription depicted as a sound wave like in the audio editor software. Some parts of the wave are annotated to show what’s being said
Text Classification Text Classification
Named Entity Recognition Named Entity Recognition
Sentiment Analysis Sentiment Analysis
Comparison Comparison
Audio-To-Text Transcription Audio-To-Text Transcription
Pick the service you need
An example of a data annotation edge case depicted as a bird next to a flower that looks like a bird. But the flower is wrongfully recognized as a bird with 92% accuracy
An example of an object recognition of a bird with 98% accuracy
Quality Assurance Quality Assurance
Model Validation Model Validation
Pick the service you need
An example of 2 LLMs generating different inferences for data annotator to choose the best response
An example of one LLM generating two different inferences for data annotator to choose the best one
An example of a user submitting a detailed prompt with a detailed response from the LLM
An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image
An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description
An image example of semantic segmentation with a title: train your LLM like Segment Anything to annotate objects on images
LLM Comparison LLM Comparison
Question-Answer Pairs Question-Answer Pairs
Prompts Generation Prompts Generation
Image and Text Alignment Image and Text Alignment
Image Captioning Image Captioning
Object Detection and Classification Object Detection and Classification
Pick the service you need

Request to gather publicly available or field data.

Pick the service you need
An example of content moderation: identified NSFW content on social media. It is censored and labeled as NSFW.

Get a dedicated content moderator or annotate your NSFW social data to train your model.

Pick the service you need

Beyond annotation, process your data with services like digitizing, parsing, and cleansing.

Pick the service you need

How It Works

Step 1

Free Pilot

Send us your dataset sample for free labeling to experience our services risk-free.

Step 2

QA

Evaluate the pilot results to ensure we meet your quality and cost expectations.

Step 3

Proposal

Receive a detailed proposal tailored to your specific text annotation needs.

Step 4

Start Labeling

Begin the labeling process with our expert team to enhance your NLP project.

Step 5

Delivery

Receive timely delivery of your labeled data, keeping your project on schedule.

Calculate Your Cost
Estimates line

1 Pick application field
2 Select annotation method
3 Specify how many objects to label
4 Check the approximate total cost
5 Run free pilot
Pick Application Field
Select Annotation Method
Amount
0 1M
Estimated Cost
$ x objects
$
Select Annotation Method
Amount
0 1M
Estimated Cost
$ x entities
$
tab-other

For other use cases

send us your request to receive a custom calculation

RUN FREE PILOT

Send your sample data to get the precise cost FREE

Reviews

What Our Clients Say

Clutch 5.0/5 27 reviews G2 Crowd 4.9/5 19 reviews
"Their flexibility and ability to move fast impress us."

Label Your Data has successfully collected data from public sources, annotated missing skeletons, and validated pre-annotations for almost 15,000 images. Although their instructions are sometimes unclear, the team has received praise for their flexibility and speed. They also communicate via email.

"Although the task is unusual and difficult, their annotation quality is incredible."

Label Your Data has delivered high-quality data labels, which have received positive client feedback. Their team produces outputs on time and solves challenges without upselling. They also communicate effectively through virtual meetings.

“I'm impressed with how easy our communication with the team is and how simple and effective the processes are."

Label Your Data's support has been crucial in developing the client's computer vision perception algorithms. They lead a simple and effective collaboration by responding quickly to concerns and questions and delivering everything on time.

"The Label Your Data team was always available for questions."

Label Your Data provided the client with high-quality annotations and added to the number of annotations in the client's portfolio. The team was consistently available for questions or updates that needed to be added to the data set. The client was impressed with the team's communication skills.

"Their flexibility and ability to work with multiple languages are impressive."

Label Your Data continues to work diligently for the client. They consistently provide training data for different document types and languages. Furthermore, they are excellent communicators and collaborators, and their ability to serve high-quality services is outstanding.

"They were always ready to work whenever we had projects."

After engaging with Label Your Data, the client has seen a reduction in error rate and success with model accuracy running on production. The team delivered on time and was communicative in their approach. The client was impressed with the steps Label Your Data took to ensure accurate QA testing.

Why AI Teams Choose Label Your Data

Data Annotation for Complex Environments

Data Annotation for Complex Environments

Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.

Structured Quality from Pilot to Production

Structured Quality from Pilot to Production

Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.

Flexible and Scalable Operations

Flexible and Scalable Operations

Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.

An Integrated Delivery Partner

An Integrated Delivery Partner

Align on goals, workflows, and expectations with a team that integrates into your process from day one.

Projects Led by Annotation Experts

Projects Led by Annotation Experts

Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.

Request a pilot

Tell us more about your project and data

Email is not valid.

Email is not valid

Company name is not valid

Phone is not valid

Some error text

Referrer domain is wrong

Thank you for contacting us!

Thank you for contacting us!

We'll get back to you shortly

Geberit Quotes

After running pilots with several annotation providers, Label Your Data delivered the strongest results by a clear margin, standing out on turnaround time, annotation quality, and the responsiveness of their feedback loops.

Quotes
Geberit
Maxime Debarbat

Maxime Debarbat

Senior ML Engineer (GenAI)

Trusted by ML Professionals

Ouster
Searidge Technologies
Zendar
Advanced Farm
ABB
Toptal
UiPath
Respeecher
Yale
Thorvald

FAQs

What is a data annotation service?

arrow

A data annotation service, like the one offered by Label Your Data, involves labeling and tagging datasets such as text, images, and videos to make them usable for training ML models. This process converts raw data into structured information, enabling AI systems to learn and improve their performance.

What is a data annotation example?

arrow

An example of data annotation is image labeling, where objects in an image are highlighted with colored layers, such as bounding boxes or segmentation masks, to identify features like cars, people, or animals. You can check out more examples in the data services section to see how we annotate different data types for various use cases at Label Your Data.

RUN FREE PILOT