Control your LLMs like never before.

Monitor and optimize your large language models with the #1 observability platform for LLMs.

Validation

Understand the impact of model and system changes with our test suite before you go to production.

How Arthur fits in:

Crafts an automated test suite from datasets with intelligent success criteria
Expedites the human-in-the-loop validation cycle by probing models for failure modes
Periodically validates models for resiliency to model changes outside their control

Safety

Prevent bad user experience and reputational impact from anomalous and malicious prompt inputs.

How Arthur fits in:

Provides deployment gates to identify anomalous inputs, PII leakage, toxicity, and other quality metrics
Learns from production performance to optimize thresholds for those quality gates

Control

Identify and analyze user feedback and model performance problems to drive better outcomes.

How Arthur fits in:

Provides core token-level observability, performance dashboarding, inference debugging, and alerting
Collects and analyzes implicit and explicit human feedback to monitor the model’s real-world impact
Accelerates ability to identify and debug underperforming regions

Explore how we’re thinking about and implementing LLMs

Downstream Fairness: A New Way to Mitigate Bias

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Announcing Arthur Shield: The First Firewall for LLMs

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How to Think About Production Performance of Generative Text

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