Powering data-centric AI development
Your one-stop data labeling platform. Exceptional speed, quality and scalability grounded in cutting-edge technologies.
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Real value to drive your AI
Get data insights fast — at a fra.
Fastest Time-to-Result
Launch your project in minutes with our preset solutions and 24/7 data labeling for fast results.
Guaranteed Quality
Place your confidence in custom-built algorithms tuned for superb data accuracy at scale.
True
Scalability
Scalability
Unlock scalability with versatile features, a highload system, and easy integration into ML pipelines.
Transparent Pricing
Pay per label with no minimums and stay under budget with significant cost savings at scale.
Versatile use cases
Pre-set data labeling pipelines for a wide variety of use cases and diverse data types, with no data minimums.
1
Image & Video Data
2
Text Data
3
Audio Data
4
Data Enrichment
5
Field Data Collection
Object Recognition & Detection
Train your computer vision model by labeling image elements with bounding boxes, polygons or key points, as well as utilizing image segmentation and tagging based on your own ontology.
Image & Video
Classification
Classification
Collect a library of annotated images or videos — grade image quality, classify by type, identify objects or content, or receive any other judgments you need.
Image
Transcription
Transcription
Transcribe and annotate the text in PDF files to train your algorithms to better identify specific parts of documents, or validate and fine-tune the output of your own OCR models.
Side-by-Side
Comparison
Comparison
Perform side-by-side comparisons to decide which option works best, check if images match descriptions, or get any other judgments you need to verify or clean up your data.
Image & Video Moderation
Make sure inappropriate content isn’t uploaded to your site. Set rules for all types of images and videos with instructions and details about what is acceptable and what is not.
Image & Video
Collection
Collection
Create collections of images and videos based on specific topics or motifs, lighting, angles or environments. Record video snippets to match your specifications.
Search
Relevance
Relevance
Evaluate the performance of your search engine, find out which ranking model works best, and improve the search algorithm.
Text
Classification
Classification
Categorize any kind of text into predefined categories or ontologies to get the labeled data you need to optimize your NLP model.
Sentiment
Analysis
Analysis
Categorize any kind of text by sentiment and reasons behind the sentiment for any purpose, from understanding customer reviews to spam filtering.
Intent
Classification
Classification
Train your chatbot, voice assistant, or any other conversational agent to better understand your user’s intent.
Utterance
Collection
Collection
Power your conversational agent with text utterances based on your instructions or scenarios.
Named Entity
Recognition
Recognition
Create your own custom ontology to identify parts of speech, classify proper nouns, or label any other entities for your project or named entity recognition (NER) model.
Audio Data Collection
Create or fine-tune a voice interface using speech samples that match your instructions. Enhance TTS (Text-to-Speech) and speech synthesis technologies with high-quality audio data.
Audio Transcription
Convert audio to text or check transcriptions for correctness. Transcribe any number of audio files quickly and accurately and use resulting data to improve speech recognition models.
Audio Classification
Detect emotion, categorize topics, or identify events in audio samples or conversations to improve your models.
Business Data
Collect information like website URLs or business hours, or categorize businesses by type, location, or size. Enrich business data and gather the information you need.
Surveys
Use a variety of survey formats to conduct surveys of thousands of people with diverse backgrounds.
Content Generation
Generate any type or any amount of content, such as product descriptions, recipes or user manuals.
Offline Data Collection
Gather or verify information about businesses like address, menu, and opening hours. Digitize offline data for your projects and business goals.
Price Monitoring
Get information about actual in-store prices on your products and competitors' discounts at specific retail outlets, then adjust your pricing to match.
Merchandising
Check where your products are placed and the allocated share of shelf space. Compare this data with information about competitors' products.
Secret Buyer
Get insights into staff performance, quality of service and demand for your products.
Foot Traffic
Learn about foot traffic and road traffic for analyzing strategic retail points.
Ad Monitoring
Collect data for analyzing and monitoring outdoor advertising. Track promo campaigns.
Unleash the potential of AI
Designed by engineers for engineers, Toloka combines cutting-edge technologies with the power of the crowd to deliver high-performing data in record time.
Smart Technologies
End-to-end solutions with multifaceted quality control, adaptive filters, and auto labeling.
Easy Integration
Free, powerful API and
open-source libraries for direct integration into automated pipelines.
Success stories
"We’ve been using Toloka for quite some time on a whole variety of tasks. What we gain is a dependable approach to data labeling that we utilize in Machine Learning models, offline metrics, and content creation and monitoring.
Thanks to Toloka, we’re able to run numerous data projects on a regular basis, which means more stable jobs for thousands of people and more potential customers for us!"
Andrei Olkhovik, crowd solutions architect, AliExpress Russia:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers.”
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos to secure biometric systems against hackers and train neural networks to distinguish real faces from fakes. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Konstantin Simonchik, science director and co-founder of ID R&D:
"Our app helps you find shoes with the best fit online – it uses a built-in 3D scanner that accurately measures your feet. Toloka helped us tremendously by collecting and labeling over 50,000 images of different human feet for our algorithm. Using this data, we were able to train our neural network to successfully separate feet from the floor, which, in turn, made our app’s 3D scanner 12% more accurate!"
Roman Kutsev, crowd solutions architect, Neatsy:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Dmitry Akimov, data engineer at VisionLabs:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“We use Toloka to annotate data for speech synthesis and recognition Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach.”
Angelina Galkevich, analyst at Tinkoff:
“Toloka is the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology.”
Anton Slesarev, head of technologies in the self-driving cars division at Yandex:
“Our service tracks outdated information on Yandex.Maps. We update business hours, add new businesses, and remove any that have closed. Toloka users perform our tasks in their neighborhoods, like taking photos of business signs with operating hours.
Every month we distribute 15 million tasks like this to 50,000 performers — this is the only way we can keep the huge Yandex.Maps database up to date.”
Yandex.Maps Team:
"We’ve been using Toloka for quite some time on a whole variety of tasks. What we gain is a dependable approach to data labeling that we utilize in Machine Learning models, offline metrics, and content creation and monitoring.
Thanks to Toloka, we’re able to run numerous data projects on a regular basis, which means more stable jobs for thousands of people and more potential customers for us!"
Andrei Olkhovik, crowd solutions architect, AliExpress Russia:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers.”
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos to secure biometric systems against hackers and train neural networks to distinguish real faces from fakes. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Konstantin Simonchik, science director and co-founder of ID R&D:
"Our app helps you find shoes with the best fit online – it uses a built-in 3D scanner that accurately measures your feet. Toloka helped us tremendously by collecting and labeling over 50,000 images of different human feet for our algorithm. Using this data, we were able to train our neural network to successfully separate feet from the floor, which, in turn, made our app’s 3D scanner 12% more accurate!"
Roman Kutsev, crowd solutions architect, Neatsy:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Dmitry Akimov, data engineer at VisionLabs:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“We use Toloka to annotate data for speech synthesis and recognition Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach.”
Angelina Galkevich, analyst at Tinkoff:
“Toloka is the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology.”
Anton Slesarev, head of technologies in the self-driving cars division at Yandex:
“Our service tracks outdated information on Yandex.Maps. We update business hours, add new businesses, and remove any that have closed. Toloka users perform our tasks in their neighborhoods, like taking photos of business signs with operating hours.
Every month we distribute 15 million tasks like this to 50,000 performers — this is the only way we can keep the huge Yandex.Maps database up to date.”
Yandex.Maps Team:
“Thanks to Toloka, we’re able to run numerous data projects on a regular basis. What we gain is a dependable approach to data labeling that we utilize in machine learning models, offline metrics, and content creation and monitoring.”
Andrei Olkhovik, crowd solutions architect, AliExpress Russia:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We were able to resolve even the most difficult cases for our customers.”
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Konstantin Simonchik, science director and co-founder of ID R&D:
"Toloka helped us tremendously by collecting and labeling over 50,000 images of different human feet for our algorithm. Using this data, we were able to train our neural network to successfully separate feet from the floor, which, in turn, made our app’s 3D scanner 12% more accurate!"
Roman Kutsev, crowd solutions architect, Neatsy:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Dmitry Akimov, data engineer at VisionLabs:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different hypotheses and then choose the optimal approach.”
Angelina Galkevich, analyst at Tinkoff:
“Toloka is the first place we go to prepare data for AI. You can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms.”
Anton Slesarev, head of technologies in the self-driving cars division at Yandex:
“Every month we distribute 15 million tasks to 50,000 performers in Toloka — this is the only way we can keep our huge database up to date.”
Yandex.Maps Team:
“Thanks to Toloka, we’re able to run numerous data projects on a regular basis. What we gain is a dependable approach to data labeling that we utilize in machine learning models, offline metrics, and content creation and monitoring.”
Andrei Olkhovik, crowd solutions architect, AliExpress Russia:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We were able to resolve even the most difficult cases for our customers.”
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Konstantin Simonchik, science director and co-founder of ID R&D:
"Toloka helped us tremendously by collecting and labeling over 50,000 images of different human feet for our algorithm. Using this data, we were able to train our neural network to successfully separate feet from the floor, which, in turn, made our app’s 3D scanner 12% more accurate!"
Roman Kutsev, crowd solutions architect, Neatsy:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Dmitry Akimov, data engineer at VisionLabs:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different hypotheses and then choose the optimal approach.”
Angelina Galkevich, analyst at Tinkoff:
“Toloka is the first place we go to prepare data for AI. You can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms.”
Anton Slesarev, head of technologies in the self-driving cars division at Yandex:
“Every month we distribute 15 million tasks to 50,000 performers in Toloka — this is the only way we can keep our huge database up to date.”
Yandex.Maps Team:
Latest news
IMDB-WIKI-SbS is a new large-scale dataset that contains 9,150 images appearing in 250,249 pairs annotated on a crowdsourcing platform.
Organizing committee of the workshop: Andrew Ng (Landing AI, DeepLearning.AI), Lora Aroyo (Google Research), Cody Coleman (Stanford University), Greg Diamos (Landing AI), Vijay Janapa Reddi (Harvard University), Joaquin Vanschoren (Eindhoven University of Technology)
Carole-Jean Wu (Facebook), Sharon Zhou (Stanford University).
