DeepRails

About DeepRails

DeepRails is an advanced AI reliability and guardrails platform engineered to enable development teams to 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 providing a comprehensive solution that not only detects but also substantively fixes these errors. The platform operates as a critical quality control layer, evaluating AI outputs across multiple dimensions including factual correctness, grounding in source material, and reasoning consistency. This allows engineering teams to distinguish critical factual errors from acceptable model variance with high precision. Built to be model-agnostic and production-ready, DeepRails integrates seamlessly with leading LLM providers and modern development pipelines. Its core value proposition lies in moving beyond mere monitoring to offer automated remediation workflows, configurable evaluation metrics aligned with specific business objectives, and human-in-the-loop feedback systems that facilitate continuous model improvement. The platform is designed specifically for AI engineers and developers who require granular control, robust audit capabilities, and the confidence to deploy AI features at scale without compromising on reliability or accuracy.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as a real-time interception layer for AI-generated content. It automatically scores model outputs against configured guardrail metrics such as correctness, completeness, and safety. Upon identifying a potential hallucination or quality issue that falls below a defined threshold, the API can trigger automated remediation actions. These actions include the "FixIt" function, which attempts to correct the output in-place, or "ReGen," which triggers a new generation from the primary LLM. This process ensures that only vetted and improved responses are delivered to the end-user, effectively functioning as a kill-switch for erroneous AI outputs before they impact the customer experience.

Five Configurable Run Modes

DeepRails provides five distinct run modes for the Defend API, allowing developers to precisely balance accuracy, detail, and computational cost. The spectrum ranges from "Fast" mode, optimized for ultra-low latency and cost, to "Precision Max Codex," which employs the deepest verification analysis for maximum accuracy in critical applications. Intermediate modes like "Precision" and "Precision Codex" offer graduated levels of scrutiny. This configurability ensures teams can apply an appropriate level of verification for each use case, from high-volume customer support chats to low-tolerance legal document analysis.

Unified Workflow Configuration & Deployment

A central feature of DeepRails is its "configure once, deploy everywhere" architecture. Teams define a single Workflow, specifying guardrail metrics, hallucination tolerance thresholds, and improvement actions. This Workflow is assigned a unique workflow_id that can be referenced across any number of applications, services, or environments (e.g., production, staging). This means a consistent quality control policy can be enforced uniformly across a website chatbot, a mobile app interface, and a Slack support bot without redundant configuration, simplifying management and ensuring reliability standards are met universally.

Comprehensive Analytics and Audit Console

The DeepRails Console provides full observability into AI performance and guardrail activity. It logs every interaction in real-time, presenting key metrics such as hallucination catch/fix rates and score distributions for correctness and safety. Engineers can drill into any individual run to view a detailed trace, including the original input, the initial model output, the evaluation scores and rationale, and the step-by-step improvement chain if remediation was applied. This creates a complete audit trail for compliance, debugging, and continuous model refinement, offering transparency into every decision made by the AI and the guardrail system.

Use Cases of DeepRails

Legal and Compliance Advisory Systems

For AI applications generating legal advice, contract summaries, or compliance documentation, factual accuracy is non-negotiable. DeepRails can be configured with low-tolerance thresholds for correctness to scrutinize citations of case law, statutory references, and procedural guidance. The platform verifies the grounding of such information and can automatically trigger corrections or flag outputs for human review, preventing the dissemination of legally erroneous information that could result in liability or non-compliance for the business.

Customer Support and Technical Chatbots

In high-volume customer support environments, chatbots must provide accurate, helpful, and brand-appropriate information. DeepRails monitors these interactions to ensure responses are factually correct regarding product features, pricing, and troubleshooting steps. It also enforces completeness and safety guardrails to maintain a positive customer experience. By automatically fixing minor hallucinations or escalating problematic outputs, the platform maintains the utility of AI-driven support while protecting brand reputation and reducing escalations to human agents.

Healthcare Information and Triage Assistants

AI systems offering preliminary healthcare information or symptom checking require an extreme degree of reliability. DeepRails enforces stringent guardrails on medical content, validating the factual basis of information against trusted sources and ensuring responses include necessary safety disclaimers. The platform's ability to block or substantively correct ungrounded medical claims is critical for patient safety and for developers to meet the rigorous compliance standards of the healthcare industry.

Financial Services and Insurance Analysis

AI tools in finance and insurance that generate reports, explain policy terms, or analyze market data must be free of speculative or invented figures. DeepRails evaluates outputs for numerical accuracy, consistency in reasoning, and grounding in provided financial documents or real-time data feeds. This ensures that clients and internal stakeholders receive dependable analysis, helping firms mitigate the risk of decisions based on AI-generated hallucinations that could have significant financial consequences.

Frequently Asked Questions

How does DeepRails differ from basic LLM output monitoring?

Basic monitoring solutions typically flag anomalies or score outputs but stop at notification. DeepRails is a full guardrails platform that integrates detection with automated, substantive remediation. Its Defend API actively intervenes to fix or regenerate problematic outputs in real-time before they reach users. Furthermore, it offers a suite of tools including customizable workflows, multiple run modes for accuracy/cost trade-offs, and a comprehensive analytics console for audit and improvement, providing a complete operational framework for AI quality control.

What types of metrics can DeepRails evaluate?

DeepRails is built with a flexible evaluation framework centered on core metrics essential for production AI. The primary metrics include Correctness (factual accuracy and grounding), Completeness (whether the query was fully addressed), and Safety (adherence to content policies). These metrics can be tuned with custom thresholds. The platform's evaluation engine analyzes the model's output against the input prompt and, where configured, against retrieved context (e.g., from web search or uploaded files) to generate these scores.

Can I use DeepRails with any LLM or AI model?

Yes, DeepRails is designed to be model-agnostic. It operates as a post-processing layer that receives the input prompt and the output generated by any primary LLM (such as those from OpenAI, Anthropic, Cohere, or self-hosted models). The platform evaluates and acts upon this text, meaning it can integrate seamlessly into existing AI stacks regardless of the underlying model provider, as long as you can pass the input and output data to the DeepRails API.

How are the hallucination tolerance thresholds determined?

DeepRails offers two primary methods for setting thresholds. For most users, the recommended "Automatic Thresholds" option uses DeepRails' adaptive algorithms to auto-calibrate sensitivity based on the historical performance and patterns observed within your specific workflow. For use cases requiring absolute control, developers can set "Custom Thresholds" per metric (e.g., setting Correctness to require a score above 0.95). This allows for fine-tuned precision based on the risk profile and accuracy requirements of each application.