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Trusted evaluation for LLM capabilities and safety
Evaluation Challenges

The State of Evaluations Today is Limiting AI Progress

Lack of high quality, trustworthy evaluation datasets (which have not been overfit on).

Lack of good product tooling for understanding and iterating on evaluation results.

Lack of consistency in model comparisons and reliability in reporting.

Why Scale

Reliable and Robust Performance Management

Scale Evaluation is designed to enable frontier model developers to understand, analyze, and iterate on their models by providing detailed breakdowns of LLMs across multiple facets of performance and safety.

Proprietary Evaluation Sets

High-quality evaluation sets across domains and capabilities ensure accurate model assessments without overfitting.

Rater Quality

Expert human raters provide reliable evaluations, backed by transparent metrics and quality assurance mechanisms.

Product Experience

User-friendly interface for analyzing and reporting on model performance across domains, capabilities, and versioning.

Targeted Evaluations

Custom evaluation sets focus on specific model concerns, enabling precise improvements via new training data.

Reporting Consistency

Enables standardized model evaluations for true apples-to-apples comparisons across models.


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 Staff

1000s of red teamers trained on advanced tactics and in-house prompt engineers enable state of the art red teaming at scale.

Content Libraries

Extensive libraries and taxonomies of tactics and harms ensure broad coverage of vulnerability areas

Adversarial Datasets

Proprietary adversarial prompt sets are used to conduct systematic model vulnerability scans.

Product Experience

Scale’s red teaming product was selected by the White House to conduct public assessments of models from leading AI developers.

Model-Assisted Research

Research conducted by Scale’s Safety, Evaluations, and Analysis Lab (SEAL) will enable model-assisted approaches.

Companies are incentivized to game leaderboards in order to one-up one another. This makes it hard to tell whether AI systems are actually improving. That’s why it’s important that organizations such as Scale assess these AI systems with their private evaluations.

Dan Hendrycks

Director, Center for AI Safety

The work Scale is doing to evaluate the performance, reliability, and safety of AI models is crucial. Government agencies and the general public alike need an independent, third party like Scale to have confidence that AI systems are trustworthy and to accelerate responsible AI development.

Dr. Craig Martell

Former Chief Digital and Artificial Intelligence Officer (CDAO), U.S. Department of Defense

Enable the safety of LLMs today!