
Toloka provides expert-curated training, post-training, evaluation, and safety/red-teaming data for AI agents, LLMs, and VLMs through a self-serve platform and managed services, combining human experts with AI-assisted quality assurance.
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Toloka AI B.V.Toloka is a provider of expertly curated training and evaluation data for AI agents and models, including LLMs and VLMs. The company builds data solutions that combine human expertise with technology to accelerate AI development across agentic skills, coding, AI safety, and multimodal generation (text, image, video, audio). Toloka offers both a self-serve platform (Toloka Platform, in beta) and managed data services. Its platform uses an AI-guided setup and always-on LLM Quality Assurance (QA) to help teams quickly configure tasks, select appropriate expert tiers, and maintain quality during labeling, generation, and evaluation. Toloka emphasizes enterprise-ready data production with security, scale, and global reach. It highlights a large expert network spanning dozens of domains and languages, alongside automated quality control and antifraud methods, and compliance with major security and privacy standards. The company also contributes to the AI community via research, benchmarks, tutorials, and collaborations, with work spanning alignment, RLHF/SFT data collection methods, evaluation metrics and benchmarks, and red-teaming methods for identifying vulnerabilities and risks.
HC score
verified business cases



Model safety and fairness evaluation, advanced red-teaming, and high-quality safety data generation for SFT, debiasing, and guardrail tuning; includes hazard cases and large-scale attack generation across many languages.
Red Teaming
Safety Evaluation
Risk Taxonomy
Managed, end-to-end data production integrating human expertise and technology for training datasets, agent environments, evaluation, red-teaming, and specialized datasets across modalities and domains.
Managed Delivery
Hybrid Pipelines
Expert Review
Purchase-ready curated datasets including Tau-bench Dataset Extension, University-level Math Reasoning Dataset, and Multimodal Conversations Dataset (e.g., 3,500+ dialogues with 4-turn image+conversation samples).
Benchmark Datasets
Multimodal Data
Expert Validated
Documentation and practices describing Toloka’s security, privacy, resilience, and industry compliance approach, including security and privacy principles and vulnerability reporting channels.
Compliance
Privacy Controls
Vulnerability Reporting
Self-serve platform providing AI-guided task setup and always-on LLM QA for RLHF/preference data, instruction tuning, model evaluation, synthetic data validation, data enrichment, and content moderation QA with automatic expert tier selection.
AI Task Setup
LLM QA
Expert Tiers

A large technology client needed domain-specific demonstrations to improve LLM performance using reinforcement learning techniques. The work required Finance (US) expertise to ensure the demonstrations reflected accurate financial context. The demonstrations also needed to be produced in English and aligned to reinforcement learning workflows. Finance (US) experts were engaged to produce English-language demonstration data tailored for reinforcement learning use. The demonstrations were created to fit the client’s RL data requirements and support model performance improvements. The delivery focused on producing a consistent set of demonstrations suitable for RL workflows. A total of 3,500 datapoints of Finance (US) demonstration data were delivered for the project. The dataset was produced in English and aligned to the client’s reinforcement learning workflow needs. This provided the domain-specific demonstrations the client required for its reinforcement learning data pipeline.
Skills
Project Details

A big tech client needed high-quality multilingual demonstrations to support RAG-focused post-training. The customer required consistent, well-edited data suitable for post-training foundational LLMs. The scope included multiple languages, increasing complexity and quality requirements. Skilled editors created multilingual demonstration datasets for the customer. The datasets were produced in English, German, and Italian to support the RAG-focused post-training work. The delivered content was prepared for use in post-training foundational LLMs. The project delivered demonstration datasets across three languages. A total of 2,500 datapoints per language were delivered for the post-training effort. The customer received multilingual demonstrations aligned to RAG-focused post-training needs.
Skills
Project Details
An independent global marketing consultancy delivering outsized growth.




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