Production-ready training and eval data. In minutes.
Describe your task. The AI assistant automatically configures the pipeline, selects the right experts, and keeps quality in check.
BETA
Trusted by Leading AI Teams
Every task in your pipeline
on one platform.
RLHF & Preference data
Expert-ranked responses and multi-turn dialogues for complex reasoning and domain-specific tasks — e.g. ranking model outputs on coding or legal reasoning.
Synthetic data validation
Verify LLM-generated training data for factuality and guideline compliance — e.g. checking synthetic RLHF pairs against ground truth.
How it works
Task description
Describe your task in plain language. Goal, quality bar, dataset.
That's all the AI needs.
Review your setup
The agent proposes quality requirements, expert guidelines, and task UI.
You review each component and approve.
Validate before you scale
Run a handful of tasks yourself before the full project goes live.
Catch anything off before it compounds.
Work begins
Experts label. LLM QA validates every output in real time.
Your feedback continuously improves the QA system for the next run.
Download results
LLM QA-verified data, formatted and ready for your pipeline.
No manual review required.
The right expert for every task.
Matched automatically
Domain experts
Specialists in law, medicine, finance, science, and 90+ domains. Use when the task requires real subject-matter knowledge or the cost of a wrong label is high.
Used for:
Complex reasoning or domain knowledge
Sensitive content or regulated fields
High-stakes model evals
General annotators
Trained generalists for tasks that need consistency and scale, not specialization. Use when quality comes from clear guidelines and good QA, not domain depth.
Used for:
Text generation and annotation
Image and video classification
Preference labeling
Global crowd
High-volume, geographically distributed workforce for straightforward data tasks. Use when speed and scale matter the most.
Used for:
Data collection
Simple annotation tasks
Сontent moderation
Built-in quality
Most platforms leave QA to you. Toloka's LLM QA system runs automatically on every output — before results ever reach your pipeline.
Before your project launches, the AI assistant helps you define and review your quality criteria.
During labeling, it validates every output in real time, flags edge cases, and iterates based on your feedback.
89.1% accuracy catching failures — before they reach your pipeline. No manual review. No engineering required.
FAQ
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What are the ideal tasks for Toloka?
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How does the quality assurance process work?
Why is user quality calibration within a project’s setup important?
How quickly can I get results?
What makes the expert tiers different?
Do I need technical experience to use Toloka?
When will Toloka be available?
What if I need additional / tech support?
Is there any onboarding required?
How can I pay for my projects?
Trusted by Leading AI Teams
Start your first project today
Enterprise quality.
Results in 24-48 hours.
Prefer data services managed for you? Our team will work with you to find the option that best suits your needs.