DeepRails

About DeepRails

DeepRails represents the next evolutionary stage in AI development, moving teams from simply deploying models to shipping truly trustworthy, production-grade AI systems. It is an AI reliability and guardrails platform built specifically for engineers and developers who are serious about quality. As large language models become core to real-world applications, the challenge has shifted from basic functionality to ensuring consistent, factual, and safe outputs. Hallucinations and incorrect information are no longer just quirks; they are critical blockers to user adoption and business viability. DeepRails tackles this head-on by providing a comprehensive suite that not only hyper-accurately identifies these issues but also substantively fixes them in real-time. The platform empowers teams to evaluate AI outputs for factual correctness, grounding, and reasoning consistency, allowing them to distinguish true errors from acceptable model variance with high precision. Beyond detection, DeepRails introduces automated remediation workflows, custom evaluation metrics aligned with specific business goals, and human-in-the-loop feedback mechanisms that create a continuous improvement cycle for model behavior. Designed to be model-agnostic and production-ready from day one, it integrates seamlessly with leading LLM providers and fits into modern development pipelines, giving teams the confidence to scale their AI initiatives with control and reliability.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as your real-time AI correction engine, sitting between your LLM and your end-user. It automatically evaluates every model output against configured guardrails for correctness, completeness, and safety. When a potential hallucination or quality issue is detected, it can trigger automated improvement actions like "FixIt" or "ReGen" to correct the output before it ever reaches the customer. This proactive defense is the core of shipping reliable AI, transforming a passive system into an actively managed one that improves with every interaction.

Five Powerful Run Modes

DeepRails offers granular control over the accuracy and cost of evaluations with five distinct run modes. Teams can choose from "Fast" for ultra-fast, low-cost checks, up to "Precision Max Codex" for the deepest, most detailed verification possible. This spectrum allows developers to perfectly match the verification depth to the criticality of each use case, whether it's a high-volume chat interaction or a low-tolerance legal document analysis, ensuring optimal resource allocation without compromising on quality where it matters most.

Full Developer Configurability & Workflows

Every aspect of DeepRails is designed for developer control. You configure "Workflows" for the Defend API, defining custom evaluation metrics, hallucination tolerance thresholds (either automatic or custom), and specific improvement actions. Once a workflow is defined, it can be referenced and deployed across any number of applications and environments—from production chatbots to staging services—ensuring consistent quality control everywhere. This "configure once, deploy everywhere" philosophy streamlines management and enforces standards.

DeepRails Console for Analytics & Audit

The DeepRails Console provides complete visibility into your AI's performance. It offers beautiful, real-time metrics on hallucinations caught and fixed, distributions for correctness and safety scores, and a full audit trail. You can drill into any individual run to see the detailed trace, the original and improved outputs, and the complete "improvement chain." This transparency is crucial for debugging, proving compliance, and understanding the evolving behavior of your AI systems in production.

Use Cases of DeepRails

Legal and Compliance Applications

For legal tech applications, the cost of a hallucination is exceptionally high. DeepRails ensures that AI-generated legal summaries, contract analyses, or case citations are factually accurate and grounded in real source material. By setting low tolerance thresholds for correctness and employing high-precision run modes, firms can leverage AI for draft generation and research assistance without risking the introduction of fabricated precedents or incorrect legal advice, thereby maintaining strict compliance and professional integrity.

Customer Support and Trustworthy Chatbots

Implementing DeepRails in customer support chatbots transforms them from potential liabilities into reliable brand assets. It continuously monitors interactions to ensure answers about product features, return policies, or technical steps are accurate and complete. When an uncertain or incorrect response is detected, the system can automatically regenerate a correct answer or flag it for human review, progressively improving the bot's knowledge base and building lasting customer trust through consistent, helpful support.

Healthcare and Medical Information Safeguards

In healthcare applications, where providing accurate information is critical, DeepRails acts as an essential safety layer. It can evaluate AI-generated patient communication, symptom checkers, or medical literature summaries for factual grounding and safety. The platform helps prevent the dissemination of harmful or misleading medical information, ensuring that AI tools support clinicians and patients with verified, reliable content that adheres to the highest standards of care.

Financial Services and Accurate Reporting

Financial institutions use AI for generating reports, explaining complex financial products, and summarizing market news. DeepRails guards these outputs against numerical inaccuracies, unsubstantiated market predictions, or misrepresentations of financial risk. By automating the verification of data grounding and reasoning consistency, it enables the safe use of AI in sensitive financial contexts, protecting both the institution's credibility and the client's financial well-being.

Frequently Asked Questions

How does DeepRails actually fix a hallucination?

DeepRails employs automated remediation workflows within its Defend API. When an output scores below the configured threshold for metrics like correctness, it can trigger actions such as "FixIt," which attempts to correct the specific erroneous part of the text, or "ReGen," which instructs the LLM to generate a completely new response. This process happens in milliseconds before the response is sent to the user, ensuring they only receive validated and improved information.

Is DeepRails tied to a specific LLM provider?

No, DeepRails is built to be model-agnostic. It is designed to work seamlessly with outputs from any major LLM provider (like OpenAI, Anthropic, Google, etc.) as well as open-source models. You send the model's output to the DeepRails API for evaluation and improvement, making it a versatile layer of reliability that can be added to any existing AI stack without vendor lock-in.

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

The Defend API is for real-time, inline correction. It evaluates and fixes outputs as they are generated before they reach the end-user. The Monitor API is for post-hoc analysis and observability. It is used to evaluate logs of past AI interactions, track performance trends, and identify issues without being in the critical request path. Together, they provide comprehensive quality control for both active prevention and historical analysis.

Can I define my own evaluation metrics?

Absolutely. A core feature of DeepRails is full developer configurability. Beyond standard metrics like correctness and safety, you can define custom evaluation metrics that are perfectly aligned with your specific business goals and quality standards. This allows you to tailor the guardrails to what matters most for your unique application, whether it's adherence to a specific brand voice, compliance with internal policies, or a unique measure of completeness.