DeepRails

DeepRails is the ultimate AI reliability and guardrails platform, built to eliminate hallucinations and errors from your production AI applications! It's the definitive kill-switch for AI hallucinations, designed specifically for developers and engineers who refuse to ship AI that makes things up. In a world where large language models (LLMs) are becoming business-critical, the risk of incorrect, ungrounded, or unsafe outputs is a major barrier to adoption. DeepRails shatters that barrier by not only detecting these issues with hyper-accuracy but also providing automated fixes in real-time. It empowers teams to evaluate AI outputs for factual correctness, grounding, reasoning, and safety with incredible precision, differentiating between true errors and acceptable model variance. With its core products—Defend API, Monitor API, and Playground—DeepRails offers complete AI quality control. From setting customizable guardrails and automated remediation workflows to providing detailed analytics and human-in-the-loop feedback, it ensures your AI systems are trustworthy, reliable, and ready for any industry, from legal and finance to healthcare and education. Stop just flagging problems and start fixing them with DeepRails!

Ultra-Accurate Hallucination Detection

DeepRails provides the most precise detection of AI hallucinations and errors on the market! It uses an expansive library of guardrail metrics—like Correctness, Completeness, and Context Adherence—to score every LLM output on a granular scale. Each metric is engineered to be significantly more accurate than alternatives, with benchmarks showing up to 53% higher accuracy than AWS Bedrock. This means you can trust DeepRails to catch subtle factual inaccuracies, missing information, and deviations from provided context before any faulty output reaches your end-user.

Automated Remediation & Fixes

DeepRails doesn't just tell you what's wrong—it actively fixes it! This is the only guardrails platform that provides actionable remediation. Through the Defend API, you can configure workflows to automatically correct issues using "FixIt" or "ReGen" actions the moment a hallucination is detected. This real-time correction engine acts as a safety net, ensuring that only verified, high-quality responses are delivered to your customers, dramatically improving user trust and system reliability without manual intervention.

Full Audit Trails & Real-Time Analytics

Gain complete visibility into your AI's performance with the DeepRails Console! Every single interaction—from your LLM, through DeepRails, to your customer—is logged in real-time. The console provides beautiful metrics, detailed execution traces, and comprehensive audit logs. You can track key guardrail metrics, view score distributions, drill into any run to see improvement chains, and monitor exactly what was caught and fixed. This transparency is essential for debugging, compliance, and continuously improving your AI models.

Customizable Guardrail Metrics & Workflows

Tailor DeepRails to your exact domain and requirements with incredible flexibility! Choose from a wide array of pre-built metrics for Quality, Safety, and Advanced evaluation, or create completely custom metrics. Configure unique workflows in minutes by setting specific hallucination thresholds and defining the improvement actions that trigger. Whether you need to validate legal citations, ensure brand tone compliance, or secure a RAG system, DeepRails adapts to provide the precise guardrails your application needs.

Legal & Compliance AI Assistants

Ensure every legal citation and piece of advice is bulletproof! For law firms and compliance teams using AI to draft documents or research cases, hallucinations can lead to serious professional repercussions. DeepRails evaluates outputs for factual correctness and grounding, automatically verifying case references and statutory interpretations against provided context. It catches and fixes erroneous legal advice before it's ever seen by a lawyer or client, making AI a trustworthy partner in high-stakes environments.

Financial Services & Advisory Chatbots

Build financial AI that never misquotes a figure or gives bad advice! In banking, fintech, and insurance, inaccurate information can result in massive financial loss and regulatory penalties. DeepRails guards chatbots and advisory tools by rigorously checking the completeness and factual accuracy of responses about market data, product terms, or financial guidance. It ensures all recommendations are fully explained and grounded in the provided data, protecting both the institution and the customer.

Healthcare Diagnosis & Patient Support Tools

Safeguard patient health with AI you can trust implicitly! Medical AI tools must be perfectly accurate and safe. DeepRails enforces strict guardrails by evaluating drug interaction lists, symptom checkers, and treatment information for correctness and safety. It detects potential hallucinations in medical explanations and filters out unsafe content, ensuring that AI-powered healthcare support is reliable, compliant, and never risks patient well-being through fabricated information.

Robust RAG (Retrieval-Augmented Generation) Systems

Supercharge your RAG pipelines with guaranteed grounding! A common failure point for RAG systems is when the LLM "goes rogue" and generates an answer not supported by the retrieved documents. DeepRails solves this with its Context Adherence metric, which strictly evaluates whether every factual claim is directly supported by the provided context. It automatically corrects ungrounded assertions, guaranteeing that your AI assistant stays faithful to its knowledge base and delivers accurate, sourced information.

How does DeepRails actually fix a hallucination?

DeepRails offers automated remediation workflows through its Defend API! When a guardrail metric scores below your set threshold (e.g., Correctness score is too low), you can configure an action like "FixIt" or "ReGen" to trigger automatically. "FixIt" might instruct the LLM to revise a specific incorrect part of the response, while "ReGen" could request a completely new response. This happens in real-time, often before the flawed output is sent to the user, seamlessly improving response quality.

What makes DeepRails more accurate than other evaluation tools?

DeepRails is built with proprietary evaluation models and metrics engineered for hyper-accuracy! The platform benchmarks its core metrics—like Correctness and Completeness—against major providers like AWS Bedrock, demonstrating superior accuracy (e.g., 45% more accurate for Correctness). This precision comes from a focused approach on detecting the nuanced differences between genuine hallucinations and acceptable model variance, reducing false positives and ensuring you only fix what's truly wrong.

Can I use DeepRails with any LLM or AI model?

Absolutely! DeepRails is model-agnostic and designed to work seamlessly with any large language model or AI system you are deploying. Through its simple API and SDKs, you can integrate the Defend or Monitor APIs into your existing application stack, whether you're using OpenAI's GPT models, Anthropic's Claude, open-source models, or any other provider. It acts as a independent quality control layer on top of your AI infrastructure.

Is there a way to test DeepRails before integrating it?

Yes! You can start building for free and explore DeepRails' capabilities in the Playground. The Playground environment allows you to experiment with different guardrail metrics, test them against sample prompts and outputs, and see the evaluation results and scoring in action. This is a perfect way to understand how the system works and configure your workflows before committing to a full API integration.