Toloka provides comprehensive data solutions tailored for the development of AI agents and Large Language Models (LLMs). By integrating human expertise with sophisticated technological automation, Toloka accelerates the AI lifecycle, from foundational training to final evaluation. The company supports a wide range of agent types, including conversational bots, corporate assistants, deep research agents, computer use agents, coding copilots, and OS-level agents. Their platform ensures high-quality data through a robust infrastructure that includes over 50 automated quality control methods and 60 platform-level anti-fraud mechanisms. With over a decade of experience, Toloka offers specialized support for creating context-rich simulated environments, preference data, reinforcement learning tasks, and safety-focused red-teaming.

Some of the key features are:

• Agentic Training & Evaluation: Provides trajectory demonstrations, step-by-step evaluations for tool-use workflows, and virtual environments for agent testing.
• Robust Quality Control: Utilizes over 50 automated quality control methods and 60 anti-fraud techniques to ensure high data integrity.
• Expert Network: Access to a global crowd of contributors from over 100 countries, with a large portion possessing advanced degrees across 90+ domains.
• Compliance-First Security: Operates with ISO 27001 and ISO 27701 certifications, while maintaining SOC 2, GDPR, CCPA, and HIPAA compliance.
• Flexible Deployment: Supports various storage configurations, including private and on-premises options, built on a Microsoft Azure infrastructure.
• Toloka Arena: An independent evaluation platform for benchmarking agentic intelligence across private, non-contaminated tasks to measure real-world performance.

Toloka operates as a strategic partner to AI teams by functioning as an extension of internal development resources. Through the combination of a scalable global workforce and advanced AI tools, they help teams define quality metrics, conduct rigorous red-teaming, and implement reinforcement learning workflows. The platform’s capacity to handle multi-format data—including text, image, video, and audio—makes it a versatile choice for organizations requiring high-precision datasets.

Some common use cases include:

• Agent Benchmarking: Using Toloka Arena to compare leading LLMs on private benchmarks to determine their real-world capabilities on tasks they have not encountered during training.
• Safety Red-Teaming: Identifying potential vulnerabilities, such as injection attacks, and ensuring policy compliance in agentic systems.
• Workflow Automation: Training corporate assistants to automate tasks by interacting with internal tools, knowledge bases, and complex enterprise policies.
• Coding Assistance: Generating production-ready code examples and full repository structures to improve the performance of AI coding copilots.
• Simulated Environment Creation: Building context-rich environments and RL-gyms with MCP replicas for evaluating agent performance in realistic settings.