DeepRails

About DeepRails

DeepRails is the definitive AI reliability platform engineered for teams who are serious about shipping trustworthy, production-grade AI. It transforms the daunting challenge of LLM hallucinations from a critical blocker into a manageable, automated process. Built by AI engineers for AI engineers, DeepRails goes far beyond simple detection. It is the only guardrails solution designed to both hyper-accurately identify factual errors, grounding issues, and reasoning inconsistencies and substantively fix them before flawed outputs ever reach your users. The platform provides complete AI quality control through a suite of products: the real-time Defend API for correction, the Monitor API for observability, and the Playground for testing. With model-agnostic integration, customizable evaluation metrics aligned with business goals, and human-in-the-loop feedback, DeepRails empowers developers to deploy AI with unwavering confidence, ensuring every interaction is accurate, safe, and reliable.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as your intelligent kill-switch, sitting between your LLM and your end-user to evaluate every output in real-time. It scores responses for correctness, completeness, and safety against your configured thresholds. When a potential hallucination or quality issue is detected, it doesn't just flag it—it can automatically trigger remediation workflows like "FixIt" to correct the text or "ReGen" to request a new response from your model, ensuring only verified content is delivered.

Five Powerful Run Modes

DeepRails offers unparalleled flexibility with five distinct run modes, allowing you to perfectly balance accuracy, speed, and cost for any use case. Choose from "Fast" for ultra-low latency needs up to "Precision Max Codex" for the deepest, most thorough verification possible. This puts you in complete control of the performance trade-offs, enabling optimized guardrails for everything from high-volume chat to critical legal document analysis.

Full Developer Configurability & Workflows

Every aspect of DeepRails is built for developer control. You configure custom Workflows that define your guardrail metrics, hallucination tolerance thresholds (using automatic adaptive algorithms or custom values), and improvement actions. Once defined, a single workflow can be referenced and deployed across any application, platform, or environment (prod, staging), ensuring consistent AI quality governance everywhere.

Deep Observability & Analytics Console

Gain complete visibility into your AI's performance with the DeepRails Console. Track key metrics like hallucinations caught and fixed, view distributions for correctness and safety scores, and drill down into the full trace of any interaction. This provides beautiful, actionable analytics and audit logs, turning every LLM run into an opportunity for continuous improvement and trust.

Use Cases of DeepRails

Legal & Compliance Advisory

Ensure every legal citation, case reference, and compliance guideline generated by your AI is factually airtight. DeepRails validates the accuracy of legal advice, automatically corrects or regenerates hallucinated case names or statutes, and provides a full audit trail. This empowers law firms and legal tech companies to deploy AI assistants that enhance lawyer productivity without risking malpractice or reputational damage.

Customer Support & Technical Chatbots

Deploy support chatbots that reliably solve customer problems without inventing information. DeepRails guards against incorrect troubleshooting steps, false product details, or ungrounded promises. By fixing hallucinations in real-time, it maintains customer trust, reduces escalations to human agents, and ensures your brand's communication is consistently helpful and accurate.

Healthcare Information & Triage

Safeguard patient well-being by ensuring AI-powered health information tools provide only verified, medically sound guidance. DeepRails scrutinizes outputs for factual correctness against trusted sources, filters out unsupported claims about treatments or symptoms, and enables human-in-the-loop review for critical cases. This allows for the safe deployment of AI in sensitive health education and non-diagnostic triage scenarios.

Financial Services & Insurance

Protect against costly errors in financial modeling, insurance policy explanations, or investment advice generated by AI. DeepRails evaluates numerical reasoning, checks the grounding of financial data, and ensures all disclaimers and regulatory language remain intact and correct. This builds the foundation of trust required to integrate LLMs into fintech platforms and client-facing financial tools.

Frequently Asked Questions

How does DeepRails actually fix a hallucination?

DeepRails employs automated remediation workflows triggered when an output fails a guardrail check. The primary method is "FixIt," where the system intelligently edits the problematic text to correct the factual error while preserving context and intent. Alternatively, the "ReGen" action can request your original LLM to generate a new response, often with an augmented prompt that includes the correction guidance. The fixed or regenerated output is then re-evaluated before being sent to the user.

Is DeepRails tied to a specific LLM provider?

No, DeepRails is proudly model-agnostic. It is designed to work seamlessly with any large language model, whether you're using OpenAI, Anthropic, Google Gemini, open-source models via API, or even your own proprietary models. You simply send the model's input and output to the Defend API for evaluation and correction, making it a universal layer of reliability for your AI stack.

What's the difference between the Defend and Monitor APIs?

The Defend API is for real-time, inline protection. It evaluates and can fix outputs during a live user interaction. The Monitor API is for observability and post-hoc analysis, allowing you to evaluate logs of past LLM interactions, track quality trends, and identify issues without being in the critical request path. Together, they provide a complete lifecycle solution for AI quality.

Can I customize what DeepRails evaluates?

Absolutely. DeepRails is built for full developer configurability. You define custom Workflows where you specify which metrics to evaluate (like correctness, completeness, safety, or custom scores), set the tolerance thresholds for each, and choose the improvement actions. This allows you to align the guardrails directly with your specific business goals and risk tolerance for each application.