Arthur AI
FreemiumThe full lifecycle platform for ensuring reliable AI.
Arthur AI is a full-lifecycle platform that helps organizations ship reliable, secure, and performant AI systems across traditional machine learning, generative AI, and agentic workflows. It provides continuous evaluation, built-in guardrails, agent discovery and governance, and real-time monitoring to catch regressions, hallucinations, and policy violations before they reach users. The platform uses a federated data plane and control plane architecture that keeps sensitive inference data inside the customer's environment while transmitting only aggregated metrics for dashboards and alerts.
What is it
A full-lifecycle AI reliability platform that provides continuous evaluation, monitoring, and governance for AI systems from development through production.
What it can do
Run pre-production and runtime evals, enforce guardrails against PII leakage, toxicity, prompt injection, and hallucinations, discover and catalog AI agents, and monitor performance across traditional ML, GenAI, and agentic workflows.
Who is it for
AI teams, product managers, compliance leaders, and executives in startups and enterprises that need to deploy trustworthy AI at scale, especially in regulated industries.
Key Features
Continuous Evaluation Engine — Lifecycle Testing
Evaluate AI systems across every stage of the lifecycle, from pre-production experiments and prompt A/B tests to always-on production evals. Arthur computes metrics for data drift, accuracy, hallucination, toxicity, PII, sensitive data, and custom business KPIs so teams can detect regressions as they happen.
Agent Discovery & Governance — Shadow-Agent Inventory
Automatically scan connected AWS, GCP, and on-prem infrastructure to discover unregistered agents, catalog their tools and LLMs, and bring them under centralized governance. This eliminates shadow AI and gives organizations a single system of record for agentic systems.
Built-in Guardrails — Runtime Protection
Block or flag unwanted outputs with out-of-the-box guardrails for sensitive data and PII detection, toxicity, prompt injection, jailbreak attempts, and hallucination. Rules support per-use-case thresholds and fast execution, with p95 latencies under 200ms for most non-LLM-Judge checks.
Prompt Management — Versioned Prompt Lifecycle
Treat prompts as first-class production assets with versioning, templating, promotion, and instant rollback. PMs and developers can update prompts without redeploying code, run structured experiments, and compare performance across versions before promoting to production.
Use Cases
Monitor Customer-Facing Chatbots
Run continuous evals on production LLM chatbots to detect hallucinations, toxic outputs, prompt injection attempts, and PII leakage before end users see them.
Govern Enterprise Agent Sprawl
Discover AI agents running across fragmented cloud environments, register them into a central inventory, and enforce consistent security and brand policies at scale.
Evaluate LLM Applications Before Release
Use structured prompt, RAG, and agent experiments with A/B testing and curated datasets to validate changes and prevent regressions from reaching production.
Track Traditional ML Model Health
Monitor classification, regression, recommender, forecasting, and computer vision models for drift, accuracy degradation, precision/recall changes, and data quality issues.
Pricing plans
Frequently Asked Questions
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