DeepRails

DeepRails is the collaborative platform for engineering teams dedicated to building trustworthy, production-grade AI applications. In an era where large language models (LLMs) are powering critical business functions, the risk of hallucinations and inaccurate outputs can undermine user trust and create operational risks. DeepRails empowers your team to move beyond simple detection and into proactive resolution. It's the only guardrails solution that not only identifies issues like factual inaccuracies and reasoning flaws with ultra-high precision but also automatically fixes them before they reach end-users. By providing granular evaluation across metrics like correctness, completeness, and safety, DeepRails helps teams distinguish between critical errors and acceptable model variance. The platform fosters a synergistic workflow with its human-in-the-loop feedback systems, automated remediation tools, and customizable guardrails that align with your specific business goals. Built to be model-agnostic and production-ready, DeepRails integrates seamlessly with major LLM providers and modern development pipelines, giving developers the confidence to ship reliable AI experiences they can stand behind.

Ultra-Accurate Hallucination Detection & Correction

DeepRails goes beyond basic flagging to provide the industry's most precise detection of AI hallucinations, factual errors, and reasoning inconsistencies. It evaluates outputs against a comprehensive suite of metrics, scoring each claim for veracity. Crucially, it doesn't just stop at detection; its integrated correction engine can automatically fix identified issues using "FixIt" or "ReGen" actions, ensuring only validated content proceeds to your customers, which dramatically improves output quality and trust.

Expansive & Customizable Guardrail Metrics Library

Teams can choose from a wide library of pre-built, battle-tested guardrail metrics tailored for quality, safety, and advanced evaluation, including Correctness, Completeness, and Context Adherence. Each metric provides a granular 0-100 score. Furthermore, the platform allows for deep customization, enabling teams to define and implement their own evaluation metrics that are perfectly aligned with unique domain requirements and specific business objectives, ensuring the guardrails evolve with your AI's role.

Real-Time Defend API & Workflow Automation

The core of DeepRails is its real-time Defend API, which acts as a correction engine in your application's flow. Teams can configure automated workflows in minutes, setting thresholds and defining improvement actions. As LLM outputs are generated, the API instantly scores them, triggers remediation if needed, and ensures only compliant responses are delivered. This automation embeds quality control directly into the deployment pipeline, enabling seamless and scalable oversight.

Comprehensive Analytics & Audit Console

DeepRails provides full visibility into your AI's performance through a detailed console. Every API interaction is logged in real-time, offering beautiful dashboards for key metrics, detailed traces of improvement chains, and complete audit logs. This transparency allows teams to collaboratively monitor trends, drill into any specific run to understand decisions, and maintain a verifiable record of all AI outputs and corrections for compliance and continuous improvement efforts.

Legal and Compliance Advisory

For legal tech applications, ensuring the accuracy of case citations, statutory references, and legal advice is non-negotiable. DeepRails' Correctness and Context Adherence metrics can automatically verify that every legal claim is grounded in provided documentation or established law, fixing hallucinations before they are presented to a lawyer or client. This prevents misinformation and upholds the rigorous standards required in legal practice, building a reliable AI co-pilot.

Financial Services and Customer Support

In banking, insurance, and finance, providing inaccurate financial figures or policy details can lead to significant liability and eroded trust. DeepRails safeguards customer-facing chatbots and internal analytical tools by evaluating the completeness and factual correctness of responses regarding account details, terms, and calculations. This ensures customers receive reliable information, and analysts get accurate data summaries, enabling trustworthy automation.

Healthcare Information and Triage

Healthcare AI assistants must provide information that is safe, correct, and contextually appropriate. DeepRails' comprehensive safety and correctness guardrails can evaluate outputs for factual accuracy regarding drug interactions, symptom analysis, and treatment information against verified medical guidelines. It can filter out unverified or harmful content, allowing clinical teams to leverage AI for administrative and informational support with greater confidence.

Education and Content Verification

Educational platforms and research tools use AI to generate summaries, answer complex questions, and create learning materials. DeepRails ensures this content is instructionally sound, factually complete, and free from invented facts. By using metrics like Ground Truth Adherence and Completeness, it verifies that AI-generated study guides or quiz answers align with the source material, maintaining academic integrity and educational value.

How does DeepRails differ from other AI evaluation tools?

Many tools only detect potential problems, leaving the team to manually handle the remediation. DeepRails is built as a collaborative platform that both detects and automatically corrects hallucinations in real-time. It offers a more expansive and accurate library of guardrail metrics, proven to outperform alternatives, and provides a full suite for workflow automation, real-time monitoring, and audit, making it a complete production-grade solution.

Is DeepRails compatible with our existing LLM providers and tech stack?

Absolutely. DeepRails is designed to be model-agnostic and integrates seamlessly with your team's existing workflow. It works effortlessly with leading LLM providers like OpenAI, Anthropic, and others via its API. The platform fits into contemporary development pipelines, requiring minimal setup to start guarding your AI outputs, fostering synergy rather than disruption in your current engineering environment.

Can we create custom guardrails for our specific domain needs?

Yes, deep customization is a core strength. While DeepRails offers a powerful library of general-purpose metrics, it also provides the tools for your team to define and implement custom evaluation metrics. This allows you to tailor guardrails precisely to your unique business objectives, domain-specific knowledge requirements, and internal compliance standards, ensuring the platform grows with your specialized use cases.

What kind of analytics and oversight does the platform provide?

The DeepRails Console offers comprehensive, real-time analytics and full audit capabilities. Your team can track key performance metrics, view distributions for scores like correctness and safety, and drill down into the complete history of any AI interaction. This includes seeing the original output, the guardrail evaluation, any triggered improvement actions, and the final delivered response, ensuring full transparency and facilitating collaborative troubleshooting.