Safety

Track model performance to detect and react to data drift, improving model accuracy for better business outcomes.

Use the power of an LLM trained or fine-tuned on your full data set while having the peace of mind from knowing that users are blocked from retrieving sensitive data from the training set.

Block responses that are not value-aligned with your organization.

Detect likely incorrect responses and prevent them from being returned to a user where they can do significant harm if they are actioned upon.

"Arthur has created the tools needed to deploy LLMs more quickly and securely, so companies can stay ahead of their competitors without exposing their businesses or their customers to unnecessary risk."

Adam wenchel

Chief Executive Officer, Arthur

Explore how we’re thinking about and implementing LLMs

ML Model Monitoring

What Does the ML Lifecycle Look Like for LLMs in Practice?

By:

Haley Massa

ML Model Monitoring

Keep the Lights On: Making Deployed AI/ML Better for Everyone

By:

John Dickerson

ML Model Monitoring

Data Drift Detection Part II: Unstructured Data in NLP and CV

By:

Karthik Rao and Rowan Cheung