DeepRails

DeepRails is the definitive guardrails platform engineered to solve the single biggest problem in production AI: hallucinations. It's built for developers and engineering teams who are serious about shipping trustworthy, reliable AI applications and refuse to accept "making things up" as an unavoidable cost of innovation. While other tools might simply flag a potential issue, DeepRails is the only solution designed to both hyper-accurately identify hallucinations and then substantively fix them in real-time. The platform provides a comprehensive suite for AI quality control, enabling teams to evaluate outputs for factual correctness, grounding, reasoning, and safety with industry-leading precision. Beyond detection, its automated remediation workflows and human-in-the-loop feedback systems actively improve model behavior. DeepRails is model-agnostic, production-ready, and integrates seamlessly into modern development pipelines, giving teams the confidence to deploy AI at scale without compromising on reliability or user trust.

Defend API - The Real-Time Correction Engine

Defend API is your proactive shield against flawed AI outputs. It acts as a middleware layer that intercepts your LLM's response, runs it through a configurable suite of guardrail metrics (like Correctness or Context Adherence), and can automatically fix issues before the response reaches your user. Using actions like "FixIt" or "ReGen," it can correct hallucinations, add missing citations, or even regenerate a compliant response, turning a potential error into a trustworthy interaction in milliseconds.

Expansive & Custom Guardrail Metrics Library

Move beyond basic moderation with a deep library of specialized evaluation metrics. Choose from purpose-built metrics like Correctness for factual accuracy, Completeness for answering all query parts, and Context Adherence for RAG systems. Each metric provides a granular 0-100 score and is benchmarked to be significantly more accurate than alternatives. You can also create fully custom metrics aligned with your specific business logic and domain requirements for unparalleled control.

DeepRails Console with Full Audit Trails

Gain complete visibility into your AI's performance with the DeepRails Console. Every interaction—from your LLM, through DeepRails' evaluation and remediation, to the final customer—is logged in real-time. The console provides beautiful dashboards for key metrics, detailed traces of every "improvement chain" where a fix was applied, and full audit logs. This turns AI reliability from a black box into a transparent, measurable, and improvable system.

Automated Remediation & Improvement Workflows

DeepRails doesn't just tell you what's wrong; it helps you make it right. Configure automated workflows that trigger specific actions when a guardrail threshold is breached. This can include sending the output for human review, invoking a tool to fetch correct data, or instructing the model to regenerate its answer. These continuous feedback loops ensure your AI systems actively learn and improve their behavior over time, directly from production data.

Legal & Compliance AI Assistants

Ensure every legal citation, case reference, and piece of advice is factually verifiable and grounded in provided documentation. DeepRails' Correctness and Context Adherence metrics prevent AI from inventing non-existent statutes or misinterpreting precedent, which is critical for maintaining professional integrity and avoiding liability in sensitive legal, financial, and compliance applications.

Healthcare and Medical Information Bots

Safeguard patient interactions by rigorously verifying medical information, drug interaction lists, and treatment advice against trusted sources. DeepRails can detect and correct subtle factual hallucinations, ensuring that AI-powered health support tools provide only accurate, grounded information and filter out unsafe or unverified content, protecting user well-being.

Robust RAG (Retrieval-Augmented Generation) Systems

Supercharge your RAG pipelines by guaranteeing that every factual claim in the AI's answer is directly supported by the retrieved context. The Context Adherence metric acts as a final verification layer, catching instances where the model might "go rogue" and insert its own knowledge or assumptions, thereby ensuring the system remains a reliable channel for your proprietary data.

Customer Support and Brand Interaction Chatbots

Maintain brand consistency and quality by enforcing instruction adherence for tone, style, and format. Ensure complex, multi-part customer queries are answered completely and that responses never leak sensitive PII or generate harmful content. This allows customer-facing AI to be both helpful and perfectly on-brand, building trust with every interaction.

How does DeepRails' accuracy compare to other services?

DeepRails is built for precision. Our core metrics are independently benchmarked and significantly outperform generalized alternatives. For example, our Correctness metric is 45% more accurate than AWS Bedrock's for factual evaluation, and our Completeness metric is 53% more accurate. This high precision reduces false positives and ensures you're only fixing real problems.

Can DeepRails work with any LLM or AI model?

Absolutely. DeepRails is designed to be completely model-agnostic. It integrates seamlessly with all leading LLM providers (like OpenAI, Anthropic, Google) and can evaluate outputs from any model, including open-source ones. It fits into your existing stack as a middleware layer, making it easy to add guardrails without changing your core AI infrastructure.

What does "fixing" a hallucination actually involve?

DeepRails offers several automated remediation actions. "FixIt" can edit the existing output to correct the inaccurate claim. "ReGen" can instruct your LLM to regenerate a new response with specific guidance to avoid the error. Other actions include routing to a human, invoking a web search for verification, or triggering a custom function. You configure the best action for each guardrail.

Is DeepRails suitable for monitoring production traffic?

Yes, DeepRails is built for production from the ground up. The Defend API handles real-time evaluation and correction at low latency. The companion Monitor API (part of the suite) is designed for high-volume logging, evaluation, and analytics of production traffic without being in the critical response path, giving you comprehensive observability.