Our comprehensive evaluations use expert knowledge to align model performance with your expectations, ensuring AI outputs are accurate, reliable, and socially responsible.
Test your model across any domains or languages
Our vetted domain experts have Masters or PhD degrees, and/or extensive work experience in their industry.
Domains
Sciences & Industries
Mathematics
Computer Science
Medicine
Psychology
Physics
Chemistry
Biology
Astronomy
Biotechnology
Bioinformatics
Law
Finance
Accounting
Economics
Teaching
Linguistics
Civil Engineering
Automotive Engineering
Religion
Language Arts
Philosophy
History
Performing Arts
Visual Arts
Languages
Spoken languages
English
French
German
Spanish
Hindi
Malay
Russian
Bengali
Filipino
Ukrainian
Vietnamese
Japanese
Tamil
Thai
Dutch
Korean
Arabic
Swedish
Turkish
Polish
We provide expert evaluation for every AI use case
Text &
Reasoning
Creative
Coding
Autonomous
agents
AI Safety
Toloka Evaluation: proven experience with top-tier models
Toloka specializes in combining human expert knowledge and tech to evaluate AI models. Our focus on real-world evaluation tasks shines a light on the true capabilities of AI models.
Custom datasets
Tailored to the domains and skills for your model's use case.
Domain specific and Skill‑Scenario based datasets
Evaluation types:
Deep knowledge domains
Industry domains
Skills
Scenarios
When to choose:
Domain specific datasets
Deep knowledge domains
Industry domains
Skill-Scenario based datasets
Specific skills performance
Interaction scenarios
Multi-modal tasks
Evaluations aligned with your goals
We target evaluations to your model's capabilities using expert human raters or automated scoring.
Human‑driven and automated evaluation
Evaluation types:
Pointwise evaluation
Side-by-side evaluation
Interactive evaluation
Red-teaming
Golden answer evaluation
Rubric-based evaluation
When to choose:
Human-driven evaluation
One-time evaluation
Creative modality
Qualitative insights by experts
Automated evaluation
Interactive evaluations
Text output
Case Studies
Long-Context Red-Teaming
Data type:
Human evaluation
Client type:
Big tech
Experts:
Skilled Editors
Language:
English
Volume:
500 datapoints
Application:
Identifying model vulnerabilities
