DeepRails

About DeepRails

DeepRails is the ultimate kill-switch for AI hallucinations, built for developers and engineers who refuse to ship AI that makes things up. In a world where large language models are powering everything from customer support to legal research, the risk of shipping incorrect, ungrounded, or flat-out fabricated outputs is a massive blocker. DeepRails tackles this head-on as a comprehensive AI reliability and guardrails platform. It doesn't just passively flag potential issues; it actively hunts down hallucinations with hyper-accurate detection and then substantively fixes them before bad responses ever reach your users. The platform provides complete quality control through its suite of tools, enabling teams to evaluate outputs for factual correctness, grounding, and reasoning consistency. It's designed to be production-ready and model-agnostic, integrating seamlessly into modern dev pipelines. With DeepRails, you move beyond hoping your AI behaves to knowing it will, giving you the confidence to ship trustworthy, production-grade AI systems you can actually stand behind.

Features of DeepRails

Defend API: The Real-Time Correction Engine

This is your frontline defense, the core engine that intercepts and remediates AI responses in real-time. You configure a workflow with your specific guardrail metrics and hallucination thresholds. When your LLM generates an output, Defend API scores it for correctness, completeness, and safety. If it falls below your threshold, it automatically triggers improvement actions like "FixIt" or "ReGen" to correct the hallucination on the fly before the response is sent to your customer. It's like having an expert editor reviewing every single AI response at lightning speed.

Ultra-Accurate Hallucination Detection & Scoring

DeepRails isn't playing guessing games. Its detection system is engineered to distinguish true, critical errors from acceptable model variance with high precision. It evaluates the core pillars of a trustworthy output: factual correctness against source materials, proper grounding in provided context, and internal reasoning consistency. This means fewer false positives and the confidence that when DeepRails flags something, it's a genuine issue that needs your attention, saving you from alert fatigue.

Five Powerful Run Modes

You have total control over the accuracy versus cost trade-off. Choose from "Fast" mode for ultra-fast, low-cost checks, all the way up to "Precision Max Codex" for the deepest, most thorough verification possible. This flexibility lets you apply surgical-grade analysis to high-stakes legal advice while using faster, leaner checks for more casual interactions, optimizing both performance and budget across your entire application.

Configure Once, Deploy Everywhere Workflows

Define your guardrail configuration once as a single, reusable workflow. Then, simply reference that workflow's ID from any service, app, or environment—be it your production website chatbot, a staging mobile app, or an internal Slack bot. This centralizes your AI quality control, ensures consistency across all user touchpoints, and makes updates a breeze. Roll out a new safety rule everywhere with one change.

Use Cases of DeepRails

Legal & Compliance Advisory Bots

For AI tools providing legal case citations or compliance guidance, a hallucination isn't an annoyance—it's a liability nightmare. DeepRails acts as the final verifying counsel, ensuring every statute, case name (like "Henderson v. Texas"), and legal principle cited is accurate and grounded in real law. It prevents the AI from inventing precedents, protecting your firm and your clients from catastrophic misinformation.

Customer Support & Technical Chatbots

Build support bots that actually solve problems instead of confusing users with made-up steps or incorrect product info. DeepRails validates that troubleshooting instructions, feature details, and policy explanations are pulled directly from your knowledge base. It automatically fixes vague or wrong answers, turning your AI support into a reliable, brand-enhancing asset that deflects tickets instead of creating them.

Healthcare and Wellness Information Systems

When users ask for symptom information, medication interactions, or general health advice, the stakes are incredibly high. DeepRails ensures all medical information is rigorously checked for factual correctness against trusted, up-to-date sources. It provides the essential guardrails to prevent the AI from generating dangerous or unverified health claims, making it a critical component for any responsible health-tech application.

Financial Services and Insurance Platforms

From explaining complex policy details to providing personalized financial calculations, accuracy is non-negotiable. DeepRails guards against AI-generated financial misinformation, verifying that numbers, terms, coverage details, and regulatory information are precisely correct. This builds essential trust with customers and mitigates the risk of costly errors in sensitive financial communications and decision-support tools.

Frequently Asked Questions

How does DeepRails actually "fix" a hallucination?

DeepRails employs automated remediation workflows. When the Defend API detects an output below your quality threshold, it can trigger actions like "FixIt," which attempts to correct the specific erroneous part of the response using the original context, or "ReGen," which can request a completely new, improved generation from your LLM. This happens in the pipeline before the response is delivered, automating the cleanup process.

Is DeepRails tied to a specific LLM provider like OpenAI?

No. DeepRails is built to be model-agnostic. It can evaluate and improve outputs from any large language model. The platform integrates seamlessly with leading LLM providers, but its core evaluation and correction logic works independently, giving you the freedom to use, switch, or combine models without changing your guardrail infrastructure.

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

The Defend API is for real-time interception and correction—it's in the request/response flow. The Monitor API is for post-hoc analysis and observability, perfect for evaluating logs, running quality audits on past conversations, or monitoring performance in environments where real-time intervention isn't needed. They use the same workflow configurations for consistency.

Can I customize what metrics DeepRails evaluates?

Absolutely. Full developer configurability is a core tenet. While it offers powerful default metrics like Correctness, Completeness, and Safety, you can define custom evaluation metrics aligned with your specific business goals and quality standards. You set the thresholds, choose the run modes, and tailor the entire system to your application's unique needs.