DeepRails

About DeepRails

DeepRails is the kill-switch for AI hallucinations, built by AI engineers for AI engineers who refuse to ship unreliable AI. As large language models (LLMs) become core to real-world products, the epidemic of "making things up" has become the single biggest blocker to trust and adoption. DeepRails solves this head-on as the only guardrails platform designed to not just hyper-accurately detect hallucinations but to substantively fix them before bad outputs ever reach your users. It's the essential reliability layer for production-grade AI, trusted by teams in legal, finance, health, and education to ship with confidence. The platform evaluates every AI output for factual correctness, grounding, and reasoning consistency, distinguishing true errors from acceptable model variance with industry-leading precision. Beyond detection, DeepRails powers automated remediation workflows, custom evaluation metrics, and human feedback loops that continuously improve your models. Fully model-agnostic and production-ready, it integrates seamlessly with leading LLM providers and modern dev pipelines, making trustworthy AI not just a goal, but a deployable reality.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API is your real-time AI correction engine, acting as a protective shield between your LLM and your customer. It automatically scores every model output against your configured guardrails for correctness, completeness, and safety. When a hallucination or quality issue is detected, it can automatically trigger "FixIt" or "ReGen" actions to correct or regenerate the response on-the-fly, ensuring only verified, high-quality outputs are delivered. This happens in milliseconds, making it invisible to your end-user but invaluable for your product's integrity.

Five Powerful Run Modes

DeepRails offers unparalleled flexibility with five distinct run modes, allowing you to perfectly balance accuracy, speed, and cost for any use case. Choose from "Fast" for ultra-low latency needs, "Precision" for high-accuracy analysis, "Precision Codex" for code-tuned verification, "Precision Max" for maximum detail, or "Precision Max Codex" for the deepest verification possible. This lets developers optimize their guardrail performance whether they're running a high-volume support chatbot or a critical legal document analyzer.

Full Developer Configurability & Workflows

Every parameter is in your hands. DeepRails is built for full developer control, allowing you to configure custom Workflows for the Defend API. You define the guardrail metrics (like correctness thresholds), set hallucination tolerance levels (using automatic adaptive algorithms or custom values), and specify improvement actions. Once configured, a single workflow can be deployed across any number of applications and environments (prod, staging) simply by referencing its workflow_id, ensuring consistent AI quality everywhere.

DeepRails Console with Live Analytics

The DeepRails Console provides beautiful, real-time analytics and complete observability into your AI's performance. Track key metrics like hallucinations caught and fixed, view distributions for correctness and safety scores, and drill down into the full trace and "improvement chain" for any individual API run. This offers not just monitoring, but full auditability, so you can understand every decision the guardrails make and continuously refine your AI's behavior.

Use Cases of DeepRails

Legal & Compliance AI Assistants

For legal tech products, hallucinations can mean citing non-existent case law or giving dangerously incorrect advice. DeepRails is deployed by legal teams to ensure every AI-generated citation, summary, or legal argument is factually grounded and correct. The Defend API can cross-reference claims against trusted databases and automatically correct inaccuracies before they are presented to a lawyer or client, mitigating severe professional and compliance risks.

Customer Support & Trustworthy Chatbots

Hallucinations in customer support bots destroy user trust and create service nightmares. DeepRails enables companies to deploy helpful, accurate chatbots at scale by ensuring every answer about product features, troubleshooting steps, or policy details is reliable. It filters out made-up information and can trigger a regeneration or a safe fallback response, turning AI support from a liability into a consistent, brand-enhancing asset.

Financial Analysis and Reporting

In finance, accuracy is non-negotiable. AI tools that generate market summaries, earnings reports, or investment insights must be free of speculative fabrications. DeepRails provides the guardrails for financial institutions to leverage AI safely, verifying numerical data, grounding statements in source documents, and ensuring all generated content meets strict factual correctness thresholds required for regulatory and internal compliance.

Healthcare Information and Triage

Healthcare applications demand extreme precision. An AI symptom checker or medical information portal cannot afford to hallucinate details. DeepRails is critical for these high-stakes environments, where it evaluates AI-generated health information for safety, completeness, and factual grounding against medical guidelines. It acts as a critical safety net, preventing the dissemination of harmful or incorrect medical information to patients or practitioners.

Frequently Asked Questions

How does DeepRails actually fix a hallucination?

DeepRails employs automated remediation workflows called "FixIt" and "ReGen." When the Defend API detects an output below your quality threshold, it can either "FixIt" by instructing a corrective model to edit and improve the existing text directly, or trigger a "ReGen" to have your primary LLM regenerate a new response, often with additional grounding instructions. The platform chooses the best action based on your workflow configuration, ensuring the final output sent to the user is corrected.

Is DeepRails tied to a specific LLM provider like OpenAI?

No, DeepRails is completely model-agnostic. It is designed to work seamlessly with any large language model, whether it's from OpenAI, Anthropic, Google, Meta, or a custom in-house model. You send your model's output to the DeepRails API for evaluation and correction, making it a universal reliability layer that fits into your existing AI stack without vendor lock-in.

What's the difference between the Defend API and the Monitor API?

The Defend API operates in real-time as a "firewall," actively evaluating and correcting outputs before they reach the user. The Monitor API, part of the broader DeepRails Suite, is for passive observation and evaluation. It's used for testing, auditing historical logs, or monitoring production outputs where real-time intervention isn't required, helping you analyze trends and improve your models without impacting user latency.

How do I set the right thresholds for hallucination detection?

DeepRails offers two approaches. For most teams, we recommend Automatic Thresholds, where our adaptive algorithms analyze your specific workflow's performance and continuously calibrate the optimal sensitivity. For total control, you can set Custom Thresholds per metric (e.g., correctness_score > 0.85). You can start with automatic settings and adjust based on the detailed analytics and false-positive/false-negative rates shown in your DeepRails Console.