DeepRails

In the grand adventure of building AI, every developer reaches a treacherous pass: the valley of hallucinations. Here, even the most powerful language models can stumble, confidently weaving fictions that threaten the integrity of applications in law, healthcare, and finance. DeepRails is the trusted guide engineered for this exact journey. It is a comprehensive AI reliability platform built for teams who are determined to ship trustworthy, production-grade AI systems. More than just a lookout that spots problems, DeepRails is a repair crew that fixes them in real-time. It meticulously evaluates every AI output for factual correctness, grounding, and reasoning consistency, providing crystal-clear scores so teams can distinguish critical errors from harmless variances. With its automated remediation workflows and seamless human-in-the-loop integration, DeepRails doesn't just guard the path; it paves a smoother road forward, continuously enhancing model performance. Designed to be model-agnostic and production-ready, it integrates effortlessly into modern development pipelines, making it the essential companion for developers across industries who refuse to let their AI make things up.

Ultra-Accurate Hallucination Detection

DeepRails employs a sophisticated, multi-metric evaluation system to detect hallucinations with industry-leading precision. It goes beyond simple keyword matching, analyzing outputs for factual correctness, completeness, instruction adherence, and grounding against provided context. Each metric delivers a granular 0-100 score, offering developers an unambiguous signal of quality and trustworthiness, proven to be significantly more accurate than alternatives like AWS Bedrock.

Automated Remediation with FixIt & ReGen

This is the core differentiator that transforms DeepRails from a monitor into an active defense system. When a hallucination or quality issue is detected above your threshold, the platform can automatically trigger corrective actions. It can either "FixIt" by editing the problematic part of the output or "ReGen" by requesting a new, improved response from your LLM, all before the flawed answer ever reaches your end-user.

Real-Time Analytics & Full Audit Traces

Every interaction between your LLM, DeepRails, and your customer is logged in real-time within the DeepRails Console. This provides beautiful, actionable dashboards showing key guardrail metrics, hallucination rates, and improvement chains. Developers can drill into any single run for a complete, detailed audit trace, enabling deep performance analysis, debugging, and compliance reporting.

Expansive & Customizable Guardrail Library

DeepRails offers a ready-to-use library of guardrail metrics tailored for critical domains, including Correctness, Completeness, and Safety. However, the journey doesn't end there. Teams can also create fully custom metrics tailored to their specific domain logic and unique requirements, ensuring the platform adapts to their application's needs rather than the other way around.

Legal Research & Citation Validation

For legal tech applications, an AI hallucinating a case name or precedent can have serious consequences. DeepRails ensures every legal citation and piece of advice is factually accurate and grounded in the provided legal documents. It automatically verifies claims and can correct erroneous references, allowing firms to deploy AI-assisted research tools with confidence.

Healthcare Information & Patient Support

In healthcare, accuracy is non-negotiable. DeepRails safeguards patient-facing AI chatbots and clinical support tools by rigorously checking medical information, drug interaction lists, and treatment advice for factual correctness and safety. It prevents the dissemination of harmful misinformation, protecting both patients and healthcare providers.

Financial Advisory & Compliance

Financial institutions using AI for reporting, analysis, or customer advice need to ensure strict compliance and truthfulness. DeepRails audits outputs for factual accuracy in data interpretation, adherence to regulatory guidelines, and completeness in answering complex, multi-part financial queries, mitigating risk and building user trust.

Robust RAG (Retrieval-Augmented Generation) Systems

For applications built on RAG architecture, ensuring the AI strictly uses the provided context is paramount. DeepRails' Context Adherence metric specifically evaluates whether each factual claim is directly supported by the retrieved documents, effectively eliminating "off-context" hallucinations and guaranteeing the integrity of the knowledge base.

How does DeepRails differ from other AI evaluation tools?

Most evaluation tools simply score or flag potential problems, leaving the developer to manually diagnose and fix them. DeepRails is built as a real-time correction engine. It not only detects issues with higher accuracy but also provides automated workflows (FixIt/ReGen) to actively remediate hallucinations before they impact the user, integrating directly into your production API flow.

Is DeepRails compatible with any LLM?

Yes, DeepRails is designed to be model-agnostic. It seamlessly integrates with all leading large language model providers, including OpenAI, Anthropic, Google, and open-source models via its SDKs and API. You can implement DeepRails into your existing pipeline without being locked into a specific model vendor.

What does the implementation process look like?

Implementation is a straightforward journey. First, you configure a workflow in the DeepRails platform, setting your guardrail metrics and thresholds. Then, you integrate the lightweight Defend API into your application's backend, routing your LLM calls through it. Finally, you monitor results and refine your rules in the real-time analytics console, often completing setup in minutes.

Can DeepRails handle custom, domain-specific rules?

Absolutely. While DeepRails offers a powerful library of pre-built metrics for general quality and safety, its true strength is extensibility. Developers can define custom guardrail metrics using their own logic and criteria, tailoring the platform's evaluation framework to the unique requirements and risks of their specific industry or application.