Test & Evaluation

Enable the safety of LLMs.

Our Methodology

What is Large Language Model Test & Evaluation?

Continuously test and evaluate LLMs, identify risks, and certify the safety of AI applications.

Continuous Evaluation

Continuously evaluate and monitor the performance of your AI systems.

Red Teaming

Identify severe risks and vulnerabilities for AI systems.

AI System Certification

Forthcoming certifications of AI applications for safety and capability against the latest standards.


Understand LLM Capabilities, Risks, and Vulnerabilities

Continuously test and evaluate LLMs, identify risks, and certify the safety of AI applications.

Features section blurred background
Model Test Evaluation 1

Model Test Evaluation 1

Improve Operational Efficiency

Automate document processing, inventory management, and intervention workflows with highly accurate AI solutions and tools.

Accelerate Speed to Delivery

Upgrade delivery times for your customers with faster decisions, better data from across your supply chain.

Reduce Compliance Risk

Accurate data extraction with intelligent document processing reduces exposure to Customs inspections, delays, and fines.


Our Approach to Hybrid Test & Evaluation

We take a hybrid approach to red teaming and model evaluation.

Hybrid Red Teaming

Red teaming seeks to elicit undesirable model behavior as a way to assess safety and vulnerabilities. The most effective red teaming pairs automated attack techniques with human experts across a diverse threat surface area.
Hybrid Red Teaming

Hybrid Model Evaluation

Continuous model evaluation is critical for assessing model capability and helpfulness over time. A scalable hybrid approach to evaluation leverages LLM-based evaluation combined with human insights where they are most valuable.
Hybrid Model Evaluation


An holistic and effective approach to model test and evaluation requires participation and coordination from a broad ecosystem of institutional stakeholders.

Key Identifiable Risks of LLMs

Our platform can identify vulnerabilities in multiple categories.


LLMs producing false, misleading, or inaccurate information.

Unqualified Advice

Advice on sensitive topics (i.e. medical, legal, financial) that may result in material harm to the user.


Responses that reinforce and perpetuate stereotypes that harm specific groups.


Disclosing personally identifiable information (PIl) or leaking private data.


A malicious actor using a language model to conduct or accelerate a cyberattack.

Dangerous Substances

Assisting bad actors in acquiring or creating dangerous substances or items(e.g. bioweapons, bombs).


Expert Red Teamers

Scale has a diverse network of experts to perform the LLM evaluation and red teaming to identify risks.

Expert Red Teamers

Red Team

Experienced Security & Red Teaming Professionals.


Coding, STEM, and PhD Experts Across 25+ Other Domains.


Specialized National Security Expertise.


Native English Fluency.


Trusted by Federal Agencies and World Class Companies

Scale was selected to develop the platform to evaluate leading Generative AI Systems at DEFCON 31, as announced by the White House’s Office of Science and Technology Policy (OSTP). We also partner with leading model builders like OpenAI to evaluate the safety of their models.

Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards.

Blueprint for an AI Bill of Rights

Office of Science and Technology Policy, White House

OpenAI threw a bunch of tasks at Scale AI with difficult characteristics, including tight latency requirements and significant ambiguity in correct answers. In response, Scale worked closely with us to adjust their QA systems to our needs.

Geoffrey Irving

Member of Technical Staff, OpenAI

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