Toloka is a human-in-the-loop data annotation platform designed to support the development of AI and machine learning models through scalable, high-quality data labeling. It provides a global crowd workforce and a robust set of tools to annotate data types such as text, images, audio, and video.

The platform is used by data scientists, ML engineers, and AI teams across industries like e-commerce, fintech, autonomous vehicles, and natural language processing. Unlike fully automated solutions, Toloka focuses on combining human judgment with automation to achieve high-quality labels at scale.

Key benefits include:

• Flexible workforce management with millions of contributors worldwide.

• Customizable task workflows for complex annotation projects.

• Reliable quality control through built-in validation tools and dynamic sampling.

What are the main features of Toloka?

Global crowd for data annotation at scale

Toloka provides access to a distributed workforce of millions of contributors from over 100 countries, enabling rapid annotation of large datasets.

• Suitable for multilingual and multicultural data needs

• Scalable for small tasks or enterprise-level projects

• Real-time task assignment and fast turnaround

Advanced task design and workflow customization

The platform offers a flexible interface for designing annotation tasks and workflows, supporting a wide range of data types and task logic.

• Supports classification, segmentation, entity extraction, transcription, and more

• Enables nested tasks, pre-labeling, and conditional logic

• Integrates with external tools via API or SDK

Built-in quality assurance mechanisms

Toloka includes a suite of quality control tools to monitor contributor performance and validate data accuracy.

• Dynamic gold standards and honeypots

• Majority vote, consensus, and model-assisted review

• Custom reviewer layers for expert validation

Real-time analytics and contributor feedback

Users can track task performance in real time, monitor annotation quality, and adjust task parameters on the fly.

• Dashboards for productivity and accuracy metrics

• Contributor feedback loops to improve instructions and task clarity

• A/B testing for task design optimization

Human-AI collaboration for complex cases

Toloka enables hybrid workflows where human annotation is complemented by automated tools or models, suitable for challenging or ambiguous cases.

• Model-in-the-loop support to pre-label or validate data

• Task escalation to expert reviewers when needed

• Ideal for domains requiring nuanced judgment or contextual understanding

Why choose Toloka?

• Highly scalable and diverse human workforce, covering a wide range of languages and domains

• Custom workflows tailored to complex annotation needs, from NLP to computer vision

• Proven quality assurance systems, ensuring reliable ground truth data

• Fast deployment and real-time management, reducing delays in ML pipelines

• Effective for ambiguous or subjective tasks, where automation alone falls short