DeepRails

DeepRails is the definitive AI reliability and guardrails platform engineered for teams that are serious about shipping trustworthy, production-grade AI systems. It acts as the essential kill-switch for AI hallucinations, moving beyond passive detection to actively and substantively fix errors in real-time. As large language models transition from prototype to product, the critical blocker shifts from capability to correctness. Hallucinations, factual inaccuracies, and inconsistent reasoning can derail user trust and business outcomes. DeepRails solves this by providing hyper-accurate evaluation of AI outputs for factual correctness, grounding, and reasoning consistency, allowing engineering teams to distinguish critical errors from acceptable model variance with high precision. Built by AI engineers for AI engineers, the platform is model-agnostic and production-ready, integrating seamlessly with leading LLM providers and modern development pipelines. It empowers developers to scale their AI applications with confidence through automated remediation workflows, custom evaluation metrics aligned with business goals, and human-in-the-loop feedback systems that continuously improve model behavior.

Defend API: Real-Time Correction Engine

The Defend API is your real-time AI correction engine, designed to detect and fix quality issues before they reach customers. It automatically identifies hallucinations and other errors, then applies corrective actions like "FixIt" or "ReGen" to remediate the output. This proactive defense layer ensures only verified, high-quality responses are delivered, building immediate user trust and reducing operational risk in production environments.

Expansive Library of Guardrail Metrics

DeepRails offers a comprehensive suite of precise, granular evaluation metrics. Choose from general-purpose metrics like Correctness, Completeness, and Context Adherence, or create custom metrics tailored to your specific domain. Each metric provides a detailed 0-100 score, with benchmarks showing significant accuracy advantages over alternatives like AWS Bedrock, enabling you to pinpoint exactly where and how your AI outputs deviate from expectations.

Monitor API & Analytics Console

Gain full observability into your AI's performance with the Monitor API and a centralized analytics console. Every interaction—from your LLM through DeepRails to your customer—is logged in real-time. The console provides beautiful metrics, detailed traces, and full audit logs, allowing teams to track performance, analyze improvement chains, and drill into any run for complete transparency and continuous optimization.

Integrated Playground for Testing

The DeepRails Playground provides a complete suite for AI quality control testing and configuration. It allows developers to experiment with guardrail metrics, set hallucination thresholds, and test improvement actions in a safe, sandboxed environment before deploying workflows to production. This accelerates development cycles and ensures your guardrails are perfectly tuned for your application's unique requirements.

Legal & Compliance Applications

Ensure every legal citation, case reference, and piece of advice generated by your AI is factually accurate and verifiable. DeepRails' high-accuracy Correctness metric is critical for legal tech applications, preventing costly hallucinations that could compromise a case or violate compliance standards, thereby protecting both the firm and its clients.

Financial Services & Advisory

Deploy AI for financial analysis, reporting, and customer advice with absolute confidence. DeepRails validates the factual grounding of all numerical data, market summaries, and investment recommendations, fixing errors in real-time to prevent the dissemination of incorrect financial information that could lead to significant monetary loss or regulatory issues.

Healthcare Information Systems

Safeguard patient interactions by verifying the accuracy of AI-generated information on drug interactions, symptom analysis, and treatment options. DeepRails' robust evaluation ensures that healthcare chatbots and support tools provide only medically sound information, directly supporting patient safety and mitigating liability for healthcare providers.

Robust RAG (Retrieval-Augmented Generation) Systems

Supercharge your RAG pipelines by guaranteeing that every factual claim in an AI's response is directly supported by the provided source documents. The Context Adherence metric is essential for preventing RAG systems from "going off script" and hallucinating, ensuring the integrity and reliability of knowledge-base assistants in enterprise and educational settings.

How does DeepRails differ from basic LLM evaluation tools?

DeepRails is engineered for production, not just prototyping. Unlike passive evaluation tools that only score outputs, DeepRails actively intervenes to correct errors in real-time via its Defend API. It combines this with a comprehensive, highly accurate metrics library, full observability, and a testing playground, creating a complete operational platform for AI quality control that scales with your application.

Is DeepRails compatible with any LLM or AI model?

Yes, DeepRails is built to be completely model-agnostic. It integrates seamlessly with all leading LLM providers and can be incorporated into any modern AI development pipeline. This flexibility ensures you can implement enterprise-grade guardrails without being locked into a specific model vendor, future-proofing your AI infrastructure.

What kind of issues can DeepRails detect and fix?

DeepRails is designed to identify and remediate a wide range of AI quality issues. Its core focus is on hallucinations and factual inaccuracies, but its metrics library also covers completeness, instruction adherence, safety violations (like PII leakage), and context grounding. The platform allows you to configure specific thresholds for each metric to match your quality standards.

How do we get started implementing DeepRails?

Getting started is straightforward. You can begin building immediately with a free tier. The process typically involves using the Playground to test and configure your guardrail workflows, then integrating the Defend and/or Monitor APIs into your application code using the provided SDKs. For complex deployments, DeepRails also offers consulting services to ensure a smooth and effective integration.