WhyLabs AI Observability

Detect, prevent, and mitigate risk in your AI applications.

WhyLabs equips organizations with AI Observability, so teams can detect and fix data & ML issues faster, improve performance and avoid costly AI failures. WhyLabs monitors ML models, LLM applications and data pipelines continuously surfacing data drift, data quality issues, performance degradation, and bias.

• Enable data and model monitoring quickly and securely: Automated monitoring and alerting across dozens of "data vitals" with out-of-the-box configurations and lightweight integrations. Cloud agnostic, built with AWS-grade privacy and security. Integration takes less than an hour.

• Deliver the impact models were designed for: Improve model performance, resilience, and auditability with alerting and reporting. Monitor model inputs, outputs, performance as well as upstream data quality in one platform.

• Reduce AI risk across the organization: improve governance, fairness, and explainability, enforce policies and guardrails.

• Operate Large Language Models safely: Extract vital telemetry from your LLM using just a prompt and a response to quickly detect and prevent malicious prompts, toxicity, hallucinations, jailbreak attempts and more.

Secure and privacy preserving - purpose-built agents analyze raw data without moving or duplicating it, ensuring privacy and security. Process 100% of the data in the most cost-effective integration. Never sample and never maintain sampling strategies.

Any data type - tabular, image, text, embeddings. Any platform - integrates with popular ML and data platforms and tools including Azure, Sagemaker, MLflow, Apache Spark, Pandas, Kafka, Ray, and more. Learn more at out https://docs.whylabs.ai/.

Enterprise-grade: SOC 2 Type 2 compliant. Essential management features like RBAC, SAML SSO, API controls.