DeepRails

About DeepRails

DeepRails is an AI reliability and guardrails platform engineered to help development teams build and ship trustworthy, production-grade AI systems. As large language models (LLMs) become integral to real-world applications, the risk of hallucinations and incorrect outputs presents a significant barrier to adoption. DeepRails directly addresses this challenge by offering a comprehensive solution that not only detects these issues with high precision but also provides the tools to fix them. The platform is designed for AI engineers and developers who require robust, model-agnostic tools to ensure their AI outputs are factually correct, well-grounded, and consistent. Its core value proposition lies in moving beyond simple flagging to offer substantive remediation, enabling teams to deploy AI with confidence. DeepRails integrates seamlessly with modern development pipelines and leading LLM providers, offering automated workflows, custom evaluation metrics, and continuous feedback loops to improve model behavior over time.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as a real-time guardrail, intercepting and evaluating AI model outputs before they reach end-users. It scores responses for critical dimensions like factual correctness, completeness, and safety against configurable thresholds. When an issue like a hallucination is detected, the API can automatically trigger remediation actions, such as using the "FixIt" tool to correct the output or "ReGen" to request a new generation from the model, ensuring only verified content is delivered.

Five Configurable Run Modes

DeepRails provides granular control over the accuracy and cost of evaluations through five distinct run modes. Teams can select from "Fast" for ultra-low latency checks to "Precision Max Codex" for the deepest, most thorough verification. This spectrum allows developers to perfectly balance the trade-off between computational expense and the required level of scrutiny for different applications, from internal tools to customer-facing production systems.

Centralized Workflow Configuration

The platform enables a "configure once, deploy everywhere" architecture. Developers can define a single guardrail workflow, specifying metrics, thresholds, and improvement actions. This workflow can then be referenced by any application or service across different environments (production, staging) and platforms (web, mobile, Slack), ensuring consistent AI quality control and simplifying management.

DeepRails Console for Analytics & Audit

Every interaction processed by DeepRails is logged in real-time to a comprehensive console. This provides teams with beautiful metrics dashboards to track key performance indicators, detailed traces of each AI run, and full audit logs. The console allows for drilling into any execution to see the complete improvement chain and evaluation rationale, enabling deep monitoring, troubleshooting, and continuous optimization.

Use Cases of DeepRails

Legal and Compliance Advisory Tools

For AI applications providing legal citations or compliance advice, accuracy is non-negotiable. DeepRails can evaluate model outputs to verify the correctness of case references, statutory interpretations, and legal reasoning. By automatically detecting and correcting hallucinations or ungrounded statements, it prevents the dissemination of legally erroneous information, mitigating risk and maintaining professional integrity.

Customer Support and Service Chatbots

In customer support, chatbots must provide reliable, factual information about products, policies, and procedures. DeepRails integrates into the chatbot pipeline to ensure every response about resetting passwords, detailing return policies, or explaining service features is accurate and complete. This builds user trust, reduces escalations to human agents, and protects the brand from the reputational damage of providing wrong answers.

Healthcare Information and Triage Assistants

AI systems in healthcare that offer symptom information or general medical guidance require extreme caution. DeepRails can enforce strict guardrails on safety and factual grounding, ensuring outputs do not contain harmful, unverified, or speculative medical claims. It helps maintain a high standard of information quality, which is critical for patient safety and regulatory compliance in the health sector.

Financial Analysis and Reporting Assistants

Financial AI tools that summarize earnings reports, generate investment insights, or explain market concepts must be free from factual error. DeepRails evaluates the numerical accuracy and logical consistency of such outputs. It can cross-verify generated data against source documents or trusted knowledge bases, ensuring that end-users, from analysts to retail investors, receive dependable financial information.

Frequently Asked Questions

How does DeepRails differ from basic output filtering?

Basic filters often rely on keyword blocking or simple rule sets, which can miss nuanced hallucinations or incorrectly block valid responses. DeepRails uses advanced evaluation models to understand the semantic meaning, factual grounding, and reasoning within an AI's output. It doesn't just filter; it scores quality across multiple dimensions and can actively improve subpar responses, providing a much more sophisticated and effective reliability layer.

Is DeepRails tied to a specific LLM provider?

No, DeepRails is built to be model-agnostic. It is designed to work seamlessly with outputs from any major LLM provider (such as OpenAI, Anthropic, Google, etc.) as well as custom or fine-tuned models. You integrate DeepRails into your application's workflow, where it evaluates the text generated by your chosen model, making it a versatile tool for any AI stack.

What are "Automatic Thresholds" for hallucination tolerance?

Automatic Thresholds are a feature where DeepRails' adaptive algorithms dynamically calibrate the scoring thresholds for metrics like correctness. Instead of requiring you to set a fixed numerical value, the system learns from the performance of your specific workflow and adjusts the sensitivity to optimally distinguish true errors from acceptable model variance, simplifying configuration and improving accuracy over time.

Can I see why DeepRails flagged or modified a specific response?

Yes, complete traceability is a core feature. For every AI run processed through the Defend API, the DeepRails Console provides a detailed audit log. This includes the original input and output, the evaluation scores and rationale for each metric, and a step-by-step trace of any improvement actions taken (like a FixIt correction). This transparency is essential for debugging and building trust in the system.