DeepRails

About DeepRails

DeepRails is the definitive AI reliability and guardrails platform engineered for development teams committed to shipping trustworthy, production-grade AI systems. In an era where large language models are rapidly transitioning from experimental prototypes to core product components, the pervasive challenge of hallucinations and erroneous outputs presents a critical barrier to adoption. DeepRails directly confronts this challenge, offering a sophisticated solution that moves beyond mere detection to provide substantive correction. The platform delivers hyper-accurate evaluation of AI outputs across critical dimensions such as factual correctness, grounding in source material, and reasoning consistency, enabling teams to distinguish genuine errors from acceptable model variance with unparalleled precision. Designed for the modern AI engineer, DeepRails is model-agnostic and production-ready, integrating seamlessly with leading LLM providers and contemporary development pipelines. It empowers organizations to implement automated remediation workflows, define custom evaluation metrics aligned with specific business objectives, and establish continuous human-in-the-loop feedback systems that refine model behavior iteratively. DeepRails is not just a monitoring tool; it is the essential infrastructure for teams who refuse to compromise on quality and demand AI they can confidently stand behind.

Features of DeepRails

The Defend API: Real-Time Correction Engine

The Defend API acts as a real-time sentinel and correction layer for your AI applications. It intercepts model outputs before they reach end-users, subjecting them to a configurable suite of guardrail evaluations. Upon detecting a hallucination or quality issue that falls below your defined thresholds, it can automatically trigger remediation actions such as "FixIt" or "ReGen" to correct the output on the fly. This ensures that only verified, high-quality responses are delivered, transforming a passive detection system into an active quality assurance mechanism.

Five Configurable Run Modes

DeepRails provides granular control over the accuracy-cost tradeoff with five distinct run modes. Teams can select from "Fast" for ultra-low latency needs, "Precision" for high-accuracy analysis, "Precision Codex" for code-tuned verification, up to "Precision Max Codex" for the deepest, most comprehensive evaluation. This flexibility allows developers to tailor the depth of verification to the specific requirements of each use case, whether it's a high-volume customer support chat or a low-tolerance legal document analysis.

Customizable Workflows & Adaptive Thresholds

The platform offers full developer configurability through customizable Workflows. You can define guardrail metrics—like correctness, completeness, and safety—set specific hallucination tolerance thresholds, and chain improvement actions. Thresholds can be set manually for total control or leverage DeepRails' "Automatic" adaptive algorithms, which self-calibrate based on real workflow performance, dynamically adjusting to maintain optimal precision.

Unified Console with Full Observability

Every interaction processed by DeepRails is logged in real-time within a unified console, providing complete observability. This offers beautiful, actionable metrics on hallucinations caught and fixed, distributions of correctness scores, and detailed run histories. Teams can drill into any individual run to see the full audit trace, including the original output, the evaluation rationale, and the steps in any improvement chain, enabling thorough debugging and performance analysis.

Use Cases of DeepRails

Legal and Compliance Advisory

For AI systems providing legal citations, case summaries, or compliance guidance, factual accuracy is non-negotiable. DeepRails ensures every referenced statute, case law, or regulatory clause is verified for correctness. It automatically flags and corrects hallucinated precedents or misrepresented rulings, mitigating severe reputational and legal risk before any advice reaches a client or internal stakeholder.

Customer Support and Technical Chatbots

In customer-facing chatbots, hallucinations can erode trust and increase support ticket volume. DeepRails integrates into support workflows to validate product information, troubleshooting steps, and policy details against trusted knowledge sources. It ensures responses are not only helpful but also factually grounded, improving customer satisfaction and deflecting unnecessary escalations.

Financial Analysis and Reporting

AI assistants generating financial summaries, market analyses, or earnings report insights must operate with impeccable accuracy. DeepRails evaluates numerical consistency, checks cited data points against source documents, and validates logical reasoning. This guards against the generation of misleading financial information that could influence erroneous business decisions or investment strategies.

Healthcare Information and Triage

While not providing medical advice, AI tools in healthcare for symptom checking, educational content, or administrative guidance require extreme care. DeepRails can be configured with low-tolerance safety and correctness guardrails to scrutinize medical terminology, procedural descriptions, and informational statements, ensuring all content is aligned with verified medical guidelines and free from harmful inaccuracies.

Frequently Asked Questions

How does DeepRails differ from basic LLM output monitoring?

Basic monitoring often merely flags potential issues or provides simple confidence scores. DeepRails is distinguished by its dual capability of hyper-accurate detection and automated, substantive correction. It doesn't just tell you an output might be wrong; it evaluates it against configurable metrics and can actively fix hallucinations in real-time before they reach the user, acting as an intelligent quality control layer.

Is DeepRails tied to a specific LLM provider?

No, DeepRails is built to be model-agnostic. It is designed to integrate seamlessly with outputs from any major LLM provider (such as OpenAI, Anthropic, Cohere, etc.) or custom models. You define the input and output structure, and DeepRails applies your configured guardrails and evaluation metrics regardless of the underlying model source.

Can I use the same guardrail configuration across different applications?

Absolutely. A core feature is the "Configure Once, Deploy Everywhere" principle. You define a single Workflow with your desired metrics and thresholds. This same Workflow ID can then be referenced by multiple applications—such as a website chatbot, a mobile app, and a Slack integration—ensuring consistent AI quality control across your entire product ecosystem from a single configuration.

What happens when DeepRails identifies and fixes a hallucination?

The process is fully transparent and logged. When a sub-threshold output is detected, the configured improvement action (e.g., FixIt) is triggered. The corrected output is then sent to the user. Simultaneously, the entire event—including the original output, the evaluation scores and rationale, and the steps taken to improve it—is recorded in the DeepRails Console for full auditability and performance analysis.