About DeepRails
What if you could peer inside your AI's reasoning process and correct its mistakes before they ever reach a user? DeepRails is an exploration into making AI systems not just powerful, but profoundly reliable. It's a comprehensive guardrails platform built for engineering teams who are curious about what their large language models (LLMs) are actually saying and determined to ship production-grade AI that doesn't hallucinate or fabricate information. At its core, DeepRails tackles the most perplexing challenge in modern AI deployment: the tendency of models to generate confident, yet incorrect, outputs. But it goes far beyond simple detection. The platform invites developers to investigate AI behavior with hyper-accurate evaluation metrics, automatically fixes identified issues, and provides deep observability into every interaction. This model-agnostic suite acts as a critical quality control layer, seamlessly integrating with your existing LLM providers to ensure every response is trustworthy, grounded, and safe. For teams venturing into domains like legal, healthcare, or finance—where accuracy is non-negotiable—DeepRails provides the essential toolkit to build with confidence and curiosity, transforming AI from a promising prototype into a dependable product.
Features of DeepRails
Defend API: The Real-Time Correction Engine
Ever wondered what happens the moment your AI generates a response? The Defend API is your live intervention layer. It meticulously scores every LLM output against your configured guardrails—like factual correctness or instruction adherence—and can automatically trigger fixes before the response is delivered. This means hallucinations are not just caught; they're actively remediated in real-time using actions like "FixIt" or "ReGen," allowing you to explore a world where AI self-corrects on the fly, ensuring only verified content reaches your end-users.
Expansive & Custom Guardrail Metrics
Dive into a rich library of evaluation metrics designed to satisfy the most inquisitive quality checks. Choose from purpose-built metrics like "Correctness" for factual accuracy, "Context Adherence" for RAG systems, or "Completeness" for thorough answers. The platform boasts significantly higher accuracy than alternatives like AWS Bedrock. But the real exploration begins when you craft custom metrics tailored to your unique business logic and domain-specific curiosities, giving you granular, score-based insights into exactly where your AI succeeds or stumbles.
Deep Observability & Audit Console
What story does your AI's output history tell? The DeepRails Console is your mission control for investigation, logging every single interaction in beautiful, detailed traces. You can track high-level performance metrics, drill down into individual runs to see the full "improvement chain" of a corrected hallucination, and audit the complete journey from your LLM, through DeepRails, to your customer. It’s built to satisfy the engineer's need to understand the "why" behind every output.
Automated Remediation Workflows
The platform empowers you to ask: "What should happen when a guardrail is triggered?" and then build the answer. Configure automated workflows that define specific improvement actions based on evaluation scores. For instance, if a "Correctness" score falls below a threshold, you can automatically query a knowledge base or trigger a web search to regenerate a factual response. This creates a dynamic, self-improving loop that continuously explores ways to enhance model behavior without manual intervention.
Use Cases of DeepRails
Legal Document & Citation Verification
Imagine a legal research assistant that confidently cites case law. How can you be sure those cases are real? DeepRails is deployed to scrutinize every generated citation for factual accuracy against provided legal databases. It detects hallucinations like fabricated case names or rulings and can automatically trigger corrections, ensuring that legal professionals receive only verified, authoritative information, protecting against critical errors in high-stakes environments.
Healthcare Information Safeguarding
Curious about deploying an AI patient support chatbot? The risk of hallucinated medical advice is paramount. DeepRails acts as a safety net, evaluating outputs for factual correctness on drug interactions, treatment protocols, and symptom advice. It simultaneously checks for PII leakage and safety violations, creating a multi-layered defense that ensures all communicated information is both accurate and compliant, building essential trust in healthcare applications.
Robust RAG (Retrieval-Augmented Generation) Systems
When building an AI that answers questions from your proprietary documents, how do you know it's not inventing details? DeepRails' "Context Adherence" metric is essential here. It investigates whether every factual claim in the AI's answer is directly supported by the retrieved source material. This ensures your RAG assistant remains faithfully grounded, turning a black-box system into a transparent and reliable source of company knowledge.
Financial Analysis & Report Generation
What happens when an AI generates a financial summary or investment insight? DeepRails allows finance teams to explore the reliability of such content. It verifies numerical data, checks the completeness of analyses against multi-part queries, and ensures all output adheres to strict compliance and formatting rules. This enables the safe automation of report generation and customer communications with a verifiable audit trail for every figure and statement produced.
Frequently Asked Questions
How does DeepRails' accuracy compare to other solutions?
DeepRails is built for precision, boasting significantly higher accuracy rates in head-to-head comparisons. For example, its Correctness metric is reported to be 45% more accurate than AWS Bedrock's equivalent, and its Completeness metric is 53% more accurate. This focus on hyper-accurate detection reduces false positives and ensures your remediation workflows are triggered by genuine issues, not acceptable model variance.
Can DeepRails work with any LLM or AI model?
Yes, one of the most explorable aspects of DeepRails is its model-agnostic design. The platform integrates seamlessly with all leading LLM providers and APIs. You can route outputs from OpenAI, Anthropic, Google, open-source models, or any other provider through the Defend API for evaluation and correction, making it a versatile tool for diverse and evolving AI stacks.
What does the "improvement chain" refer to in the audit logs?
The improvement chain is a fascinating trace that shows the complete lifecycle of an AI response. If DeepRails detects an issue and triggers a fix, the console logs the original LLM output, the evaluation scores, the specific remediation action taken (e.g., "web_search"), and the final corrected output sent to the user. This provides a transparent, step-by-step audit trail for investigating how and why any response was modified.
Is DeepRails only for detecting factual hallucinations?
While factual correctness is a flagship capability, the platform invites you to explore a much broader spectrum of AI quality. Its library includes metrics for safety (PII, hate speech), instruction adherence (tone, format), completeness, and agentic performance. This allows teams to set guardrails for brand voice, data privacy, structured output formatting, and overall response quality, not just factual grounding.