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Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
AML and fraud compliance platform pairing transaction monitoring, screening, and explainable AI agents for financial institutions.
Compliance automation platform that continuously monitors controls and evidence to help companies achieve SOC 2, ISO 27001, and HIPAA.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
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No public pricing
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Real-time transaction monitoring and rule engine
- ✦Explainable AI forensics agents
- ✦Dynamic risk scoring
- ✦Watchlist/sanctions/PEP screening
- ✦AI-native case management
- ✦Automated SAR filing to FinCEN and 70+ GoAML countries
- ✦Continuous automated compliance monitoring across frameworks
- ✦Automated evidence collection and audit preparation
- ✦Personnel access and permissions management
- ✦Vendor and third-party risk assessment workflows
- ✦Automated security questionnaire responses
- ✦Public-facing trust center for compliance status
- ✦400+ tool integrations and an API for custom workflows
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →AML compliance and monitoring
- →Reducing false-positive alerts
- →Streamlining fincrime investigations and SAR filing
- →Preparing for and maintaining SOC 2 or ISO 27001 certification
- →Automating responses to customer security questionnaires
- →Managing vendor security reviews at scale
- →Centralizing risk management across a growing company
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects