AI Safety & Red Teaming

Strengthen your model's trustworthiness
and safety in just a few weeks.

Strengthen your model's trustworthiness and safety in just a few weeks.

Our red teamers expose model vulnerabilities. After risk evaluation, our experts

apply SFT, debiasing, and guardrail tuning to prepare your model for deployment.

Trusted by Leading AI Teams

Why is Red Teaming
necessary?

Why is Red Teaming necessary?

Prevents harmful function calls

Mitigates crime, terrorism,
and misinformation

Prevents harmful, biased,
or offensive responses

Aligns with AI safety regulations

Identifies future risks

AI safety with Toloka

We provide evaluation and data annotation data services for safe and robust AI model development.

From rapid diagnostics to comprehensive evaluations, we identify areas for improvement — and generate high-quality

data for training, customized to your team’s chosen methods, including Supervised Fine-Tuning (SFT) and other techniques.

Evaluation of model
safety & fairness

  1. Proprietary taxonomy of risks to develop
    broad and comprehensive evaluations

  2. Niche evaluations developed by domain experts to consider regional and domain specifics

  3. Advanced red-teaming techniques to identify and mitigate vulnerabilities

Data for safe AI development

  1. Throughput sufficient for any project size

  2. Scalability across all modalities (text, image,
    video, audio) and wide range of languages

  3. Skilled experts trained and consent to work
    with sensitive content

Prompt attacks we can generate for your model

Discover more about your model
with Toloka red teamers

Discover more about your model with Toloka red teamers

3000+

hazard cases

3000+

hazard cases

10000+

attacks generated

per week

10000+

attacks generated

per week

35%

prompts resulting

in safety violation

35%

prompts resulting

in safety violation

40+

languages

40+

languages

Make your model trustworthy

First results in 2 weeks

Red teaming in action

Start-up

Technologies

Our red teamers generated attacks targeting brand safety for an online news chatbot

Text-to-text

Text-to-text

Generation & Evaluation

Generation & Evaluation

1k prompts, 20% Major Violations Identified

1k prompts, 20% Major Violations Identified

2 weeks

2 weeks

Non-Profit

Technologies

Our experts built a broad scope attack dataset, contributing to the creation

of a safety benchmark

Text-to-text

Text-to-text

Generation

Generation

12k prompts

12k prompts

6 weeks

6 weeks

Big Tech

Technologies

We red-teamed a video generating model, creating attacks across 40 harm categories

Text and image-to-video

Text and image-to-video

Generation & Evaluation

Generation & Evaluation

2k prompts, 10% Major Violations Identified

2k prompts, 10% Major Violations Identified

2 weeks

2 weeks

FAQ

FAQ

How can I make my AI model more trustworthy?

How can I make my AI model more trustworthy?

What is AI safety and why is it important?

What is AI safety and why is it important?

What is the difference between AI safety and AI alignment?

What is the difference between AI safety and AI alignment?

How is AI governance related to AI safety?

How is AI governance related to AI safety?

What is Red Teaming and how does it contribute to AI safety?

What is Red Teaming and how does it contribute to AI safety?

What are the key areas of AI safety research?

What are the key areas of AI safety research?

What are some of the potential risks associated with advanced AI systems?

What are some of the potential risks associated with advanced AI systems?

What safety measures can AI developers and organizations implement?

What safety measures can AI developers and organizations implement?

Trusted by Leading AI Teams