About DeepRails
DeepRails is an advanced AI reliability and guardrails platform engineered for developers and teams deploying production-grade AI systems. It directly tackles the critical barrier of LLM hallucinations and inaccurate outputs, which undermine trust and adoption. Unlike solutions that merely detect problems, DeepRails provides hyper-accurate detection coupled with automated remediation to actively fix errors before they reach end-users. The platform offers a comprehensive suite for AI quality control, centered on three core products: Defend API for real-time correction, Monitor API for observability, and Playground for testing. It evaluates outputs against metrics like factual correctness, context adherence, and safety, differentiating between critical errors and acceptable variance. Designed to be model-agnostic and production-ready, DeepRails integrates seamlessly with existing LLM providers and development pipelines. Its core value proposition is empowering engineering teams to ship trustworthy, reliable AI applications they can confidently stand behind.
Features of DeepRails
Ultra-Accurate Hallucination Detection
DeepRails provides industry-leading accuracy in identifying LLM hallucinations, significantly outperforming alternatives. It uses a granular scoring system (0-100) across multiple guardrail metrics to precisely detect factual inaccuracies, unsupported claims, and reasoning inconsistencies. This allows teams to pinpoint genuine errors versus acceptable model behavior with high confidence.
Automated Remediation & Fixes
This is the key differentiator: DeepRails doesn't just flag issues, it fixes them. Through its Defend API, the platform can automatically trigger remediation workflows like "FixIt" or "ReGen" to correct hallucinated content in real-time before the response is delivered to the customer, ensuring only verified outputs reach end-users.
Expansive Guardrail Metrics Library
Teams can choose from a broad library of pre-built evaluation metrics or create custom ones. Categories include Quality (Correctness, Completeness), Safety (PII, hate speech), and Advanced (Agentic Performance). Each metric is tuned for specific domains like legal, finance, or healthcare, providing tailored oversight.
Production-Ready Analytics & Audit
Every AI interaction processed through DeepRails is logged in real-time to a comprehensive console. This provides full audit trails, detailed performance metrics, and visualizations of improvement chains. Engineers can drill into any run to understand exactly how an output was scored and corrected, ensuring complete transparency.
Use Cases of DeepRails
Legal & Compliance Applications
Ensure AI-generated legal advice, contract summaries, or case citations are factually accurate and grounded in real statutes. DeepRails verifies legal references and prevents the hallucination of non-existent cases, which is critical for maintaining compliance and professional integrity in high-stakes environments.
Financial Services & Advisory
Deploy AI for financial analysis, report generation, or customer advice with confidence. The platform validates numerical data, investment recommendations, and regulatory information against provided context, preventing costly errors and misinformation in a tightly regulated industry.
Healthcare Information Systems
Safeguard patient-facing AI chatbots and diagnostic support tools. DeepRails checks medical information, drug interaction lists, and treatment advice for factual correctness and completeness, mitigating the risk of harmful hallucinations that could impact patient safety.
RAG (Retrieval-Augmented Generation) Systems
Enhance the reliability of RAG pipelines by enforcing strict context adherence. DeepRails ensures that every factual claim in an AI's answer is directly supported by the retrieved source documents, preventing the model from "going off-script" and inventing unsupported information.
Frequently Asked Questions
How does DeepRails fix a hallucination?
DeepRails offers automated remediation workflows. When its Defend API detects a hallucination that crosses a set threshold, it can trigger actions like "FixIt," which attempts to correct the specific inaccurate claim, or "ReGen," which instructs the LLM to regenerate the entire response. This happens in real-time within the API call flow.
Is DeepRails model-agnostic?
Yes. DeepRails is designed to work seamlessly with any major LLM provider (like OpenAI, Anthropic, etc.). You can integrate it into your existing pipeline regardless of the underlying model, allowing you to maintain consistency in guardrails and evaluation even if you switch or use multiple models.
What makes your detection more accurate than others?
DeepRails's metrics are specifically engineered for high precision in detecting nuanced hallucinations. Benchmarks provided show significant accuracy advantages over services like AWS Bedrock (e.g., 45% more accurate for Correctness). This is achieved through specialized evaluation models and fine-tuned scoring mechanisms.
Can I create custom evaluation metrics?
Absolutely. While DeepRails offers an expansive library of pre-built guardrails, you can also define custom metrics tailored to your specific business objectives, domain knowledge, and unique quality thresholds. This ensures the platform evaluates outputs based on what matters most to your application.