Arthur ensures AI reliability, security, and performance through offering robust continuous evaluation capabilities. Arthur helps AI teams test, monitor, and improve AI systems across the entire lifecycle, from development to deployment. Evals and guardrails available on the Arthur Platform are both out-of-the-box and customizable to ensure organizations can ship high-quality, trustworthy AI at scale.

Arthur also supports teams through the Agentic Development Lifecycle (ADLC), enabling developers to evaluate every step of an agent’s workflow, from providing comprehensive visibility into agent tracing to optimizing for architecture and tool use in order to ensure reliable outputs. With Arthur, teams can quantify and compare agent behavior, identify regressions, and enforce policies in real time. The result is a flywheel foundation for building and iterating on AI agents that perform reliably in production.

Unlike point solutions that focus on a single model type, Arthur delivers a unified platform for traditional, generative, and agentic AI. Whether you’re measuring drift and accuracy in machine learning models, hallucination and data security in generative systems, or groundedness and tool selection in AI agents, Arthur provides a consistent framework for evaluation and monitoring. While the platform supports organizations running thousands of AI use cases, it also delivers meaningful value even if you’re monitoring just one thanks to its robust, configurable evaluation engine and enterprise-grade analytics.

Arthur is built for AI-driven organizations of all sizes, from startups to Fortune 100s, that need to ensure their AI systems are reliable, secure, and compliant.

The Platform is trusted across regulated industries like banking, healthcare, and insurance, where oversight, auditability, and data protection are essential.

• For AI teams: including developers, product managers, and AI leaders (VPs of AI, Heads of Data, etc.) Arthur provides the tools to evaluate, monitor, and improve models and agents across the lifecycle.
• For executives and compliance leaders, such as CISOs, CIOs, and CDOs, Arthur delivers reporting and visibility into performance, risk, and policy adherence across all AI initiatives.

Arthur empowers both technical teams and business leaders to build, deploy, and govern AI responsibly.

Continuous evaluation means testing, monitoring and improving AI systems at every stage of their lifecycle, from pre-production to runtime and live deployment.

Continuous evaluation is critical because AI systems evolve with new data, user behavior, and model updates. Without continuous evaluation, performance can drift, guardrails can weaken, and reliability or compliance risks can go unnoticed. By continuously evaluating, teams ensure their AI remains accurate, safe, and aligned with business and regulatory goals over time.

Arthur monitors the full spectrum of AI systems: Traditional Machine Learning, Generative AI, and Agentic AI through a unified, consistent framework.

• Traditional ML: Metrics such as data drift, classification accuracy, precision & recall, and regression error.
• Generative AI: Evals for sensitive data handling (PII, custom/fine-tuned sensitive data), acceptable use policy (toxicity, prompt injection), deterministic evaluation (regex, keyword) and hallucination detection.
• Agentic AI: Evals for groundedness, tool selection, trace visualization, and response relevance.

This unified approach enables teams to monitor and govern all AI workloads, from models to agents, with the same reliable, scalable platform.

Arthur integrates seamlessly with existing AI workflows through an API-first design, letting you manage projects, models, metrics, alerts, and jobs via REST from your services and CI/CD. You can deploy the Evals Engine in your own environment (Docker/Kubernetes in your cloud or on‑prem) and trigger evaluations from pipelines, with a quickstart in the repo and docs. For GenAI and agents, add runtime guardrails—hallucination, prompt injection, toxicity, PII/sensitive data, and regex/keyword checks—as middleware, and monitor agents via standardized OpenTelemetry (OTEL); agent traces and outcomes are tracked alongside model metrics to improve reliability. Arthur also supports traditional ML by computing and comparing tabular metrics (drift, accuracy, precision/recall, F1, AUC) and visualizing them in dashboards with alerts, making GenAI plus traditional monitoring as simple as linking a database table or other data source. Data ingestion is supported via connectors, and incidents can be routed via webhooks (including Slack) into your workflow tools. Enterprise needs are covered with SSO (OIDC), role‑based access, and flexible deployment options (SaaS, on‑prem, or major clouds/marketplaces).

Arthur handles data security and compliance through its federated control plane/data plane architecture, ensuring that sensitive data never leaves the customer’s environment. The data plane operates securely within the customer’s VPC or on-prem environment, where all evaluations and monitoring occur. Only aggregated metrics and metadata are sent to the control plane for centralized management and visualization. This is particularly valued by enterprises that are either multi-LoB, multi-national, regulated, or some combination of the three.

Arthur also supports both single-tenant and multi-tenant SaaS deployments, giving teams flexibility based on their security and isolation requirements. Arthur can also offer a standard Business Associate Agreement (BAA), which can be executed upon request to support HIPAA-aligned and other regulated use cases.

Arthur meets rigorous security, privacy, and compliance standards, including SOC 2 Type II and enterprise data residency policies, while maintaining full visibility and control across AI systems.

The Arthur Platform provides out-of-the-box guardrails, with an emphasis on guardrails that are broadly useful within an enterprise context, such as: sensitive data handling (PII, custom/fine-tuned sensitive data), acceptable use policy (toxicity, prompt injection), deterministic evaluation (regex, keyword), prompt injection, and hallucination detection. What is unique about Arthur’s guardrails:

• Fine-grained tuning/thresholding of rules - many of Arthur’s guardrails provide custom configuration that allows users to set a per-use case threshold on where guardrails trigger
• Complimentary/adjacent definitions for use-cases - many customers have different or unique definitions of what a guardrail means within their context (i.e. toxicity), and Arthur’s guardrails give users a degree of control over fine-tuning/customizing guardrail enforcement across different use-cases
• Highly performant execution - Arthur guardrails have been tuned to support extremely fast execution, in most cases (where the enforcement isn’t using off LLMJudge) the p95 latencies of rule validation is less than 200ms

Arthur Platform (full platform)

• What it is: The hosted UI and API for managing projects, data sources, models, guardrails, eval definitions, dashboards, and alerts.
• What it does: Configure and schedule evaluations, review results, collaborate, set access controls, and route incidents via webhooks (e.g., Slack/Jira).
• Who uses it: Product, data/ML, and governance teams to manage and observe GenAI, agentic, and traditional ML in one place.

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Arthur Evals Engine (data plane)

• What it is: A deployable runner (e.g., Docker/Kubernetes) that executes evaluations and guardrail checks in your environment.
• What it does: Pulls jobs you define in the Platform, computes metrics for GenAI/agentic workflows (hallucination, prompt‑injection, toxicity, PII, etc.) and traditional ML (performance/drift), and pushes back results/aggregates.
• Why it matters: Keeps raw data in your network, fits CI/CD and data pipelines, and scales with your infrastructure—no inbound connections required.

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How they work together

• Define and schedule in the Platform → Engine runs the jobs on your data → results flow back to the Platform for visualization, alerting, and integrations.

Arthur’s evaluations are highly customizable, built to adapt to the unique goals, data, and oversight needs of every AI team.

Today’s AI isn’t one-size-fits-all. Every organization measures success differently, which is why Arthur introduced Custom Evals: a flexible capability that lets users define, configure, and reuse their own performance and quality metrics across both machine learning and generative AI systems.

Teams can:

• Create custom metrics using SQL or Python, from explainability and data health to GenAI scorers and “LLM-as-a-Judge” evaluations.
• Visualize and monitor these metrics directly within dashboards, track trends, and set alerts for deviations.
• Version, reuse, and govern metrics across teams and projects with full RBAC and auditability.

For agentic AI, Arthur enables custom, domain-specific LLMJudge evaluations, allowing teams to quantify groundedness, relevance, or tool selection accuracy for their specific agents.

Arthur’s customizable evaluations empower organizations to measure what truly matters. From drift to domain-specific performance, Arthur supports teams by operationalizing and executing evals that are relevant for organizations’ use cases. 

Arthur offers both SaaS and Enterprise options, each API-first and designed to meet teams where they are, from early startups to highly regulated Fortune 100s.

• SaaS: The SaaS version is self-serve and ready to use immediately. Teams can sign up, invite collaborators, and connect their first model in minutes through Arthur’s intuitive UI or APIs. It’s ideal for organizations that want to get started quickly with built-in security, flexible integrations, and access to Arthur’s full suite of evaluation and guardrails capabilities, all without managing infrastructure.
• Enterprise: The Enterprise deployment is API-first but fully customizable for scale, security, and compliance. It can be deployed in a customer’s VPC, on-prem, or as a dedicated single-tenant environment, with configurable SLAs, compliance guarantees, and data residency options. During the Proof of Concept phase, Arthur’s Forward Deployed Engineering and Professional Services teams work closely with customers to tailor integrations, data pipelines, and evaluation workflows to enterprise requirements.

Arthur offers flexible deployment options to meet the security, compliance, and operational needs of any organization.

Arthur’s federated control plane / data plane architecture ensures that sensitive data never leaves the customer’s environment. The data plane runs securely within your VPC or on-prem infrastructure, where all evaluations and monitoring occur locally. Only aggregated metrics and metadata are transmitted to the control plane for centralized management, visualization, and governance.

Arthur supports both single-tenant and multi-tenant SaaS deployments, giving organizations the flexibility to choose the right balance of isolation, scalability, and cost efficiency. For regulated industries such as healthcare and finance, Arthur also provides a standard Business Associate Agreement (BAA) that can be executed upon request to support HIPAA-aligned and other compliance requirements.

You can get started on the multi-tenant SaaS version Arthur today! 

This architecture allows Arthur to integrate seamlessly into existing cloud or hybrid environments while maintaining enterprise-grade security, data residency, and performance.

Arthur goes beyond traditional observability and evaluation tools with a federated architecture, unified model coverage, and enterprise-grade design built for scale and compliance.

• Architecture: Arthur’s federated control plane/data plane design keeps sensitive data within the customer’s environment while enabling centralized visibility, policy enforcement, and analytics. This allows organizations to meet strict security, privacy, and compliance requirements without sacrificing monitoring depth or speed.
• Unified Coverage: Arthur is built to monitor all types of AI systems: traditional ML, generative AI, and agentic AI, all in one platform. It provides a consistent way to evaluate and improve everything from predictive models to LLMs and autonomous agents, enabling teams to manage diverse workloads through a single interface.
• Enterprise- First: Unlike many tools on the market today, Arthur was built for the Fortune 100, supporting large, regulated enterprises across finance, healthcare, and insurance with SOC 2 compliance, RBAC, and auditability. Over time, Arthur has expanded to serve teams of all stages and sizes, offering the same reliability, flexibility, and depth of insight to emerging startups as it does to global enterprises in regulated industries.

Arthur also stands apart because it was founded by the former VP of AI at Capital One and built by a team of experts with decades of experience in applied, academic, and enterprise AI, bringing deep technical and industry knowledge to help organizations operationalize AI safely, responsibly, and effectively.