Toloka provides specialized AI training data for complex models, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). They leverage a global network of AI tutors to generate high-quality, diverse datasets for applications like coding copilots and conversational agents.
Funding
$72M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing advanced AI models, particularly those requiring sophisticated training methodologies like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), necessitates high-quality, diverse, and expertly curated datasets. The creation of these datasets is often a bottleneck, demanding specialized human expertise and scalable data annotation infrastructure.
Solution
Toloka provides a comprehensive platform for generating specialized AI training data, addressing the complexities of SFT and RLHF dataset creation. Leveraging a global network of over 6,000 AI tutors with diverse domain expertise and language proficiencies, Toloka delivers scalable, high-quality data solutions. The platform integrates advanced automation and human oversight to accelerate the development lifecycle of machine learning models, from agentic skills to AI safety. This approach ensures that AI models are trained with data that reflects real-world nuances and complex reasoning requirements.
Target Audience
Toloka serves AI development teams, machine learning engineers, and researchers across industries that require robust and scalable data annotation services to train and evaluate advanced AI models.
Features
- Global crowd of over 6,000 AI tutors with advanced degrees and specialized skills for nuanced data annotation.
- Expertise in generating data for various AI applications including Computer Use Agents, Deep Research Agents, Coding Copilots, and Conversational Agents.
- Specialized datasets for SFT and RLHF, including preference data, demonstrations, and step-by-step reasoning chains.
- Multi-format content collection capabilities for text, image, video, and audio data.
- Professional annotation and quality filtering processes, including automated quality control methods and antifraud algorithms.
- Development of benchmarks and research projects focused on AI evaluation, safety, and responsible AI practices, such as Beemo and U-MATH.
- Platform infrastructure designed for scalability, with over 50 automated quality control methods and 61 platform-level antifraud measures.
- Compliance with industry standards including ISO 27001, ISO 27701, SOC 2, GDPR, CCPA, and HIPAA.