DeepRails

About DeepRails

DeepRails is the definitive kill-switch for AI hallucinations, engineered for developers and teams who refuse to ship unreliable AI. In an era where large language models are powering everything from customer support to legal research, the specter of fabricated facts and inconsistent reasoning remains the single biggest barrier to trustworthy, production-grade adoption. DeepRails confronts this challenge head-on as a comprehensive AI reliability and guardrails platform. It doesn't just passively monitor or flag potential issues; it actively defends your application's integrity. The platform hyper-accurately evaluates AI outputs for factual correctness, grounding, and reasoning consistency, enabling teams to distinguish critical errors from benign model variance with unprecedented precision. More importantly, it provides the tools to substantively fix these hallucinations in real-time through automated remediation workflows before flawed outputs ever reach an end-user. Built to be model-agnostic and production-ready, DeepRails integrates seamlessly with leading LLM providers and modern development pipelines, offering custom evaluation metrics, human-in-the-loop feedback systems, and full auditability. This is the essential toolkit for engineering teams committed to deploying AI systems they can genuinely stand behind.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as your real-time AI correction engine, intercepting model outputs before they reach customers. It scores responses against configurable metrics like correctness, completeness, and safety. When a hallucination or quality issue is detected above your threshold, it can automatically trigger remediation actions like "FixIt" to correct the output or "ReGen" to request a new one. This creates a dynamic safety net that actively improves response quality in your live production environment, turning a passive check into an active defense layer.

Five Powerful Run Modes

DeepRails offers granular control over the accuracy-cost tradeoff with five distinct run modes. Choose from "Fast" for ultra-low latency and cost, "Precision" for high-accuracy analysis, "Precision Codex" for codex-tuned verification, "Precision Max" for maximum detail, and "Precision Max Codex" for the deepest verification possible. This flexibility allows engineers to tailor the guardrail intensity to the specific needs of each use case, from a casual chatbot to a high-stakes legal advisor, ensuring optimal performance without unnecessary overhead.

Full Developer Configurability & Workflows

Every parameter is in your hands. The platform centers on configurable Workflows where you define guardrail metrics, hallucination tolerance thresholds (either custom or automatic adaptive ones), and improvement actions. Once a workflow is defined, its unique workflow_id can be deployed across any number of applications and environments—from your website chatbot to mobile app and Slack bot—ensuring consistent AI quality control everywhere. This "configure once, deploy everywhere" philosophy streamlines management and enforcement of your reliability standards.

DeepRails Console with Real-Time Analytics

The DeepRails Console provides complete visibility into your AI's performance. Every interaction processed by the Defend API is logged in real-time, flowing into beautiful dashboards that track key metrics like hallucinations caught and fixed. You can drill down into any run for full audit details, including the original output, the evaluation scores, and the complete improvement chain if remediation was triggered. This transforms AI reliability from a black box into a transparent, measurable, and continuously improvable system.

Use Cases of DeepRails

Legal and Compliance Advisory

For AI tools providing legal case citations, contract analysis, or compliance guidance, a single hallucination can have severe consequences. DeepRails is critical here, rigorously evaluating the factual correctness and grounding of every legal reference or statement. It ensures the AI doesn't invent non-existent precedents like "Henderson v. Texas (2018)" and can automatically correct or flag unsubstantiated claims, maintaining the integrity and trust required in the legal field.

Customer Support and Technical Chatbots

Hallucinations in support chatbots can mislead users, damage brand trust, and increase escalations. Implementing DeepRails guardrails on support workflows ensures that answers to common questions—like password resets or troubleshooting steps—are accurate, complete, and safe. The automated FixIt actions can correct minor inaccuracies in real-time, guaranteeing customers receive reliable, helpful information without human intervention, scaling quality support efficiently.

Healthcare Information and Triage

In healthcare applications, where AI might offer symptom information or basic triage advice, accuracy is non-negotiable. DeepRails monitors outputs for safety and factual grounding against trusted medical sources. It prevents the model from generating harmful or unverified medical claims, acting as a essential safety layer that ensures all automated patient-facing information is vetted for reliability, protecting both users and healthcare providers.

Financial and Insurance Services

AI in finance and insurance must provide precise calculations, correct policy details, and accurate financial guidance. DeepRails guards these systems by evaluating outputs for reasoning consistency and completeness. It can detect when an AI miscalculates a quote or misstates a policy term, triggering an automatic correction or regeneration. This ensures compliance, reduces financial risk, and builds user confidence in automated financial advisors and claim processors.

Frequently Asked Questions

How does DeepRails differ from basic LLM output filtering?

Unlike simple keyword filters or content moderators that just block unsafe words, DeepRails performs a deep, semantic evaluation of factual correctness, grounding, and reasoning. It doesn't just flag; it understands the context and substance of an error. Most importantly, it provides automated remediation workflows to actually fix hallucinations (via FixIt or ReGen) in real-time, turning detection into a proactive quality improvement engine rather than a passive alert system.

Is DeepRails tied to a specific LLM provider like OpenAI?

No, DeepRails is built as a model-agnostic platform. It is designed to evaluate and improve outputs from any large language model, whether it's from OpenAI, Anthropic, Google, Meta, or open-source models you host yourself. This flexibility allows you to maintain consistent reliability and guardrail standards across a multi-model strategy, future-proofing your AI infrastructure as the ecosystem evolves.

What does "configure once, deploy everywhere" mean?

It means you define your AI quality standards—metrics, thresholds, improvement actions—in a single DeepRails Workflow. This workflow receives a unique ID. You can then reference this same workflow_id in all your different applications (website, mobile app, internal tools) and across all environments (production, staging). Any update to the central workflow configuration is instantly propagated everywhere, ensuring uniform guardrail enforcement and drastically simplifying management.

Can I set my own thresholds for what constitutes a hallucination?

Absolutely. DeepRails offers full developer configurability. You can set custom, fixed thresholds for each evaluation metric (e.g., correctness must be above 0.85) for total control. Alternatively, you can use the recommended "Automatic Thresholds," where DeepRails' adaptive algorithms analyze your workflow's performance data to dynamically calibrate and suggest optimal tolerance levels, balancing precision and recall automatically over time.