DeepRails

About DeepRails

DeepRails is the definitive AI reliability and guardrails platform engineered for development teams determined to ship trustworthy, production-grade AI systems. The core challenge with integrating large language models (LLMs) into real applications is their propensity for "hallucinations"—generating confident but incorrect or ungrounded information. This issue erodes user trust and blocks enterprise adoption. DeepRails directly solves this by providing a comprehensive suite that not only detects these critical errors with high precision but also actively fixes them before flawed outputs reach end-users. Built by AI engineers for AI engineers, it is a model-agnostic platform that integrates seamlessly into modern development pipelines. It empowers teams to move beyond merely monitoring for problems to implementing substantive, automated corrections. With capabilities like custom evaluation metrics, human-in-the-loop feedback, and detailed analytics, DeepRails provides complete AI quality control, enabling businesses in sensitive sectors like legal, finance, and healthcare to deploy AI with confidence and stand behind every response.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as a real-time kill-switch for AI hallucinations. It sits between your LLM and your user, instantly evaluating each model output for factual correctness, grounding, and reasoning consistency. When an output falls below your configured quality thresholds, the API can automatically trigger remediation workflows—such as "FixIt" to correct the response or "ReGen" to have the model generate a new one—ensuring only verified, high-quality information is delivered.

Five Configurable Run Modes

DeepRails offers granular control over the accuracy versus cost trade-off with five distinct run modes. Teams can select from "Fast" for ultra-low latency needs to "Precision Max Codex" for the deepest, most thorough verification possible. This flexibility allows developers to apply the appropriate level of scrutiny for each use case, from casual chatbots to mission-critical analytical tools, optimizing both performance and operational costs.

Unified Workflow Configuration & Deployment

Define your guardrail configuration once as a reusable Workflow and deploy it universally across any application or environment. The same workflow_id can power your production website chatbot, staging environment, mobile app, and internal Slack bot simultaneously. This "configure once, deploy everywhere" philosophy ensures consistency, simplifies management, and accelerates the rollout of AI features across your entire platform.

Comprehensive Analytics & Audit Console

Every interaction processed by DeepRails is logged in real-time to a detailed console. This provides full visibility into key metrics like hallucination rates, correctness scores, and improvement chain effectiveness. Teams can drill into any individual run to see the complete trace, evaluation rationale, and remediation steps, creating an indispensable audit trail for debugging, compliance, and continuous model improvement.

Use Cases of DeepRails

Legal and Compliance Advisory Tools

For AI applications providing legal citations or compliance guidance, inaccuracies are unacceptable and carry significant risk. DeepRails ensures every referenced case law, regulation, or statutory detail is factually verified. It automatically corrects or flags hallucinated legal precedents, allowing firms to leverage AI for research and drafting without compromising on the absolute accuracy required in the legal field.

Customer Support and Technical Chatbots

Hallucinations in support chatbots can mislead customers, damage brand reputation, and increase escalations. Implementing DeepRails guardrails ensures that answers to common questions—like password resets, troubleshooting steps, or policy details—are grounded in the company's actual documentation. It fixes incorrect instructions in real-time, leading to faster resolutions and higher customer trust.

Financial and Insurance Document Analysis

When AI is used to summarize financial reports, explain policy terms, or calculate figures, numerical and factual precision is paramount. DeepRails evaluates outputs for consistency and grounding against source documents. It catches and rectifies subtle errors in data interpretation or calculations before they lead to erroneous financial advice or incorrect claim assessments.

Educational Content and Tutoring Systems

AI tutors must provide factually correct information to be effective learning tools. DeepRails safeguards educational applications by verifying the accuracy of historical dates, scientific explanations, mathematical solutions, and language translations. This ensures students receive reliable information, maintaining the integrity of the educational process and supporting positive learning outcomes.

Frequently Asked Questions

How does DeepRails differ from basic LLM output monitoring?

Basic monitoring often just flags potential issues or uses simplistic keyword checks. DeepRails goes far beyond detection by performing hyper-accurate, multi-dimensional evaluation (factual correctness, grounding, reasoning) and then taking substantive action. Its core value is in automated remediation—actively fixing errors via FixIt or ReGen workflows—and providing the deep analytical context needed to understand and improve model behavior continuously.

Is DeepRails tied to a specific LLM provider like OpenAI or Anthropic?

No, DeepRails is built to be completely model-agnostic. It can evaluate and improve outputs from any large language model, whether you're using OpenAI's GPT models, Anthropic's Claude, open-source models via API, or even your own fine-tuned models. This flexibility allows you to maintain your preferred model stack while adding a universal layer of reliability.

What does "hallucination tolerance threshold" mean and how should I set it?

The tolerance threshold is the minimum score an AI output must achieve on a metric (like Correctness) to pass the guardrail. DeepRails offers two approaches: "Automatic Thresholds," where its adaptive algorithms calibrate the ideal level based on your workflow's performance, or "Custom Thresholds," where you manually set a fixed value (e.g., 0.85) for total control based on your specific risk tolerance for a given application.

Can I use DeepRails during the development and testing phase?

Absolutely. The "configure once, deploy everywhere" model is designed for this. You can define and refine your Workflows using the Playground and SDKs during development, apply them in your staging environment via the Defend API, and then use the exact same configuration with confidence in production. The Monitor API can also be used to passively evaluate outputs without blocking them, perfect for testing.