DeepRails

About DeepRails

DeepRails is an AI reliability and guardrails platform engineered for developers and engineering teams building production-grade AI systems. Its core mission is to act as the definitive kill-switch for LLM hallucinations, enabling teams to ship trustworthy AI by not just detecting but substantively fixing incorrect outputs. The platform directly addresses the critical blocker of model hallucinations and factual inaccuracies that hinder real-world AI adoption. DeepRails provides a model-agnostic suite that integrates seamlessly into modern development pipelines, offering real-time evaluation of AI outputs for factual correctness, grounding, and reasoning consistency. It distinguishes itself by moving beyond mere flagging to offer automated remediation workflows, allowing teams to configure custom evaluation metrics aligned with specific business goals. With features like human-in-the-loop feedback and comprehensive analytics, DeepRails ensures continuous improvement of model behavior, giving engineering teams the confidence and control needed to deploy AI at scale without compromising on reliability or accuracy.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API is the core real-time engine that intercepts and evaluates LLM outputs before they reach end-users. It scores responses against configured metrics like correctness, completeness, and safety. Upon detecting an issue that breaches a defined threshold, it can automatically trigger remediation actions such as "FixIt" to correct the output or "ReGen" to request a new generation from the model, ensuring only vetted content is delivered.

Five Configurable Run Modes

DeepRails offers granular control over the accuracy-cost tradeoff with five distinct run modes: Fast, Precision, Precision Codex, Precision Max, and Precision Max Codex. Engineers can select the mode that fits their use case, from ultra-fast, low-cost evaluations to maximum-accuracy analyses that employ deeper verification chains and specialized codex-tuned models for the most critical applications.

Unified Workflow Configuration & Deployment

Teams can define a guardrail workflow once in the DeepRails console and deploy it universally across multiple applications and environments (e.g., production, staging). By simply referencing a single workflow_id, the same quality controls can be applied to website chatbots, mobile apps, and internal Slack bots, ensuring consistent AI behavior and simplifying configuration management.

DeepRails Console with Full Audit Trails

The platform provides a comprehensive console that delivers real-time analytics, detailed metrics on hallucinations caught and fixed, and performance distributions. Every API interaction is logged with full traceability, including the complete improvement chain and audit details for every run, enabling thorough monitoring, debugging, and compliance reporting.

Use Cases of DeepRails

Legal and Compliance Advisory Bots

For AI systems providing legal citations or compliance advice, hallucinations pose severe reputational and legal risks. DeepRails ensures every piece of legal information is fact-checked and grounded in real statutes or case law before being presented to a user, automatically correcting or regenerating unsafe outputs to maintain absolute accuracy and reliability.

Customer Support and Technical Chatbots

In customer-facing support applications, providing incorrect troubleshooting steps or product information erodes trust. DeepRails integrates into the chatbot pipeline to validate the factual correctness and completeness of support answers, fixing hallucinations in real-time to ensure customers receive only accurate and helpful guidance.

Financial Analysis and Reporting Tools

AI tools that generate financial summaries, market analyses, or report data cannot afford numerical or factual errors. DeepRails guards these outputs by evaluating the reasoning consistency and grounding of financial statements, automatically remediating inaccuracies to produce reliable, audit-ready financial insights.

Healthcare Information and Triage Systems

In healthcare applications, where inaccurate information can have direct consequences, DeepRails provides a critical safety layer. It rigorously evaluates AI-generated health information for safety and factual correctness against trusted sources, ensuring that any preliminary guidance or information shared is substantively correct and safe for the end-user.

Frequently Asked Questions

How does DeepRails differ from basic output filtering or moderation APIs?

Unlike simple keyword filters or content moderation tools that only flag profanity or toxicity, DeepRails performs deep, semantic evaluation of factual correctness, grounding, and logical consistency. It doesn't just detect problems; it provides a framework for automated, substantive fixes through configurable improvement actions, making it a proactive reliability layer rather than a passive filter.

Is DeepRails tied to a specific LLM provider or model?

No, DeepRails is built to be model-agnostic. It is designed to integrate seamlessly with outputs from any major LLM provider (OpenAI, Anthropic, Cohere, etc.) or custom models. You can send the model's output to the Defend API for evaluation and remediation, making it a versatile tool for any tech stack.

Can I customize the evaluation metrics and thresholds?

Yes, full developer configurability is a core principle. You can define custom metrics relevant to your business goals and set precise thresholds for each. You can choose between automatic, adaptively calibrated thresholds or set your own custom values, giving you complete control over the sensitivity and behavior of your guardrails.

How is the DeepRails platform integrated into an existing application?

Integration is achieved via the DeepRails SDKs or direct API calls. After configuring a workflow in the console, you insert a call to the Defend API in your application logic between your LLM call and the point where you send the response to the user. The platform is designed to fit into modern, scalable development pipelines with minimal disruption.