DeepRails

DeepRails is a fundamental platform for AI reliability and guardrails, built to help engineering teams deploy trustworthy, production-grade AI systems. At its core, it addresses the foundational challenge of AI hallucinations—instances where large language models (LLMs) generate incorrect or fabricated information. DeepRails goes beyond simple detection by providing a complete solution that not only identifies these inaccuracies with high precision but also actively fixes them before flawed outputs reach end-users. The platform is designed for developers and AI engineers who require robust, model-agnostic tools to ensure their AI applications are correct, safe, and reliable. It offers meticulous evaluation of outputs for factual correctness, grounding, and reasoning, allowing teams to distinguish between critical errors and acceptable variances. With features like automated remediation workflows, customizable evaluation metrics, and seamless integration into existing development pipelines, DeepRails provides the essential building blocks for creating AI systems that you can confidently stand behind.

Ultra-Accurate Hallucination Detection

DeepRails employs advanced evaluation metrics to detect hallucinations and inaccuracies in AI-generated content with exceptional precision. The platform scores outputs on a granular scale from 0 to 100 across multiple dimensions, such as factual correctness and context adherence. This allows developers to pinpoint exactly where and how an AI model is deviating from the truth, providing a clear, measurable understanding of output quality that is more accurate than many alternative services.

Automated Remediation & Correction

Unlike basic monitoring tools that only flag problems, DeepRails provides built-in solutions to correct detected issues. Through its Defend API, the platform can automatically trigger actions like "FixIt" or "ReGen" to rectify hallucinations in real-time before the response is sent to the customer. This proactive correction engine is a foundational feature that transforms passive observation into active quality control, ensuring end-users receive reliable information.

Customizable Guardrail Metrics

The platform offers an expansive library of pre-built guardrail metrics for quality, safety, and advanced agentic performance. Teams can choose from general-purpose checks like Correctness and Completeness or create entirely custom metrics tailored to their specific domain and business objectives. This flexibility ensures that the evaluation criteria are perfectly aligned with what matters most for the application, whether in legal, healthcare, finance, or any other field.

Full Audit Trails & Analytics

Every interaction processed through DeepRails is logged in real-time, providing complete visibility into AI performance. The console offers beautiful metrics, detailed traces, and comprehensive audit logs for every run, from the original LLM call through the guardrail evaluation and final output. This transparency is crucial for debugging, performance tracking, and maintaining accountability in production AI systems.

Legal Document and Citation Verification

In the legal domain, accuracy is non-negotiable. DeepRails can be used to verify legal citations, case law references, and the factual claims within AI-generated legal summaries or arguments. By applying high-precision Correctness and Ground Truth Adherence metrics, firms can ensure their AI legal assistants do not hallucinate case names, rulings, or details, protecting against professional liability and maintaining rigorous standards.

Healthcare Information Safety

For healthcare applications providing drug information, symptom analysis, or treatment advice, factual errors can have serious consequences. DeepRails safeguards these systems by evaluating outputs for factual accuracy and comprehensive safety, detecting potential hallucinations in medical content. This ensures patients and practitioners receive only verified, reliable information from AI health aides.

Robust RAG (Retrieval-Augmented Generation) Systems

DeepRails is essential for building reliable RAG pipelines. Its Context Adherence metric specifically evaluates whether every factual claim in an AI's answer is directly supported by the provided source documents. This prevents the LLM from "going off script" and inventing information, guaranteeing that the assistant's responses are strictly grounded in the authorized knowledge base.

Customer Support and Financial Advice

In customer-facing roles within finance or support, AI must provide complete and accurate information. DeepRails uses Completeness and Instruction Adherence metrics to ensure AI agents fully answer multi-part customer queries and follow all formatting and compliance rules. This results in higher quality interactions, reduces misinformation risk, and builds greater user trust in automated services.

What exactly does DeepRails do?

DeepRails is an AI reliability platform that acts as a quality control layer for applications using large language models. Its primary function is to detect and correct hallucinations—factual inaccuracies or fabrications in AI-generated text. It evaluates every AI output against customizable guardrails for correctness, safety, and completeness, and can automatically fix identified issues before the response is delivered to your end-user.

How does DeepRails fix a hallucination?

DeepRails offers automated remediation workflows through its Defend API. When a hallucination or quality issue is detected above a set threshold, the platform can trigger predefined actions. The two main actions are "FixIt," which attempts to correct the specific inaccurate part of the output, and "ReGen," which instructs the AI model to generate a completely new response. This happens in real-time within the API call flow.

Is DeepRails compatible with any LLM?

Yes, DeepRails is model-agnostic. It is designed to work seamlessly with any large language model provider, including OpenAI, Anthropic, Google, Meta, and open-source models. You integrate the DeepRails API into your application's workflow, where it intercepts, evaluates, and potentially corrects the output from whichever LLM you are using before passing it back to your application.

What kind of metrics can I evaluate with DeepRails?

DeepRails provides a wide library of evaluation metrics. Key categories include Quality metrics (like Correctness, Completeness, Instruction Adherence), Safety metrics (detecting PII, harmful content), and Advanced metrics (like Context Adherence for RAG). You can use these pre-built, high-accuracy metrics or define completely custom metrics based on your specific needs and domain expertise.