DeepRails

About DeepRails

DeepRails is an advanced AI reliability and guardrails platform engineered to empower development teams to build and deploy trustworthy, production-grade AI systems. Its core mission is to directly address one of the most significant barriers to enterprise AI adoption: the propensity of large language models (LLMs) to produce hallucinations, factual inaccuracies, and inconsistent reasoning. Unlike basic monitoring tools that merely flag potential issues, DeepRails is architected to both hyper-accurately identify these critical failures and substantively fix them in real-time. The platform provides comprehensive evaluation of AI outputs across key dimensions such as factual correctness, grounding in source material, and logical consistency, enabling teams to distinguish true errors from acceptable model variance with high precision. Built by AI engineers for AI engineers, it is a model-agnostic solution that integrates seamlessly with leading LLM providers and modern development pipelines. DeepRails combines automated remediation workflows, customizable evaluation metrics aligned with specific business objectives, and human-in-the-loop feedback systems to create a continuous improvement cycle for model behavior, ensuring that AI applications are both reliable and safe for end-users.

Features of DeepRails

Defend API: Real-Time Correction Engine

The Defend API acts as a real-time interception layer for AI outputs. It automatically evaluates every model response against configured guardrails for correctness, completeness, and safety. When a hallucination or quality issue is detected above a defined threshold, the API can proactively fix the problem using its "FixIt" or "ReGen" improvement actions before the flawed output ever reaches the customer. This provides an active defense mechanism, transforming passive monitoring into an automated correction system.

Five Configurable Run Modes

DeepRails offers granular control over the accuracy and cost profile of evaluations through five distinct run modes. Teams can select from "Fast" (ultra-fast, lowest cost) to "Precision Max Codex" (maximum accuracy with deep verification), allowing them to tailor the depth of analysis to the criticality of the use case. This flexibility ensures optimal resource allocation, from high-volume, lower-risk interactions to low-volume, high-stakes scenarios requiring the utmost veracity.

Unified Workflow Configuration & Deployment

A central, powerful feature is the ability to define a guardrail workflow once and deploy it universally. A single configuration powers both the Defend and Monitor APIs and can be referenced across multiple production applications, staging environments, and different platforms (e.g., web chatbot, mobile app, Slack bot) using a unique workflow_id. This ensures consistent AI quality control and simplifies management across an entire organization's AI portfolio.

Comprehensive Analytics & Audit Console

The DeepRails Console provides full visibility into AI performance with beautiful, real-time metrics, detailed traces, and complete audit logs. Every interaction processed by the platform is logged, allowing teams to track key metrics like hallucinations caught and fixed, score distributions for correctness, and drill down into any individual run to see the full improvement chain and evaluation rationale, enabling robust debugging and compliance reporting.

Use Cases of DeepRails

Legal and Compliance Advisory Systems

For AI tools providing legal citations, case summaries, or compliance advice, factual accuracy is non-negotiable. DeepRails can be configured with high-precision run modes and strict correctness thresholds to evaluate every legal statement. It automatically detects and corrects hallucinated case names, rulings, or statutes, preventing the dissemination of legally erroneous information that could have serious professional consequences.

Customer Support and Technical Chatbots

In customer-facing support applications, AI must provide accurate, helpful, and brand-safe information. DeepRails workflows ensure support chatbots are grounded in knowledge bases and do not invent product features or incorrect troubleshooting steps. By fixing hallucinations in real-time, it maintains customer trust, reduces escalations to human agents, and protects brand reputation.

Financial Research and Reporting Assistants

AI systems that analyze financial data, generate reports, or summarize earnings calls must be factually precise. DeepRails validates numerical consistency, checks the grounding of financial statements against source documents, and ensures reasoning about market trends is logically sound. This use case is critical for maintaining integrity in an industry where errors can lead to significant financial loss.

Healthcare Information and Patient Triage

While not providing medical advice, AI in healthcare contexts must be exceptionally reliable. DeepRails can enforce strict safety and correctness guardrails on systems that provide general health information or symptom checking. It prevents the model from generating unverified or potentially harmful medical claims, ensuring outputs are appropriate, cautious, and aligned with trusted source materials.

Frequently Asked Questions

How does DeepRails differ from basic LLM output monitoring?

Basic monitoring tools typically flag outputs based on simple heuristics or confidence scores, often leading to high false-positive rates. DeepRails goes far beyond flagging by performing a multi-dimensional evaluation (correctness, grounding, reasoning) with high accuracy to distinguish true hallucinations from benign variations. Crucially, it then provides automated remediation to fix the identified issues, acting as an active quality control layer rather than a passive alert system.

What does "model-agnostic" mean in practice?

Model-agnostic means DeepRails does not depend on or require a specific underlying LLM (like GPT-4, Claude, or Llama). It operates on the text output of any model. You can integrate the DeepRails API into your existing pipeline regardless of which LLM provider or custom model you are using, allowing you to maintain and improve reliability across your entire AI stack without vendor lock-in.

Can I customize the evaluation metrics?

Yes, DeepRails is built for full developer configurability. While it offers core metrics like Correctness, Completeness, and Safety, you can define and align evaluation parameters with your specific business goals and risk tolerance. You configure custom thresholds for each metric and design automated improvement actions (like web search augmentation or regeneration) that trigger based on your unique workflow logic.

How does the "Configure Once, Deploy Everywhere" workflow work?

You define a single guardrail configuration, called a Workflow, within the DeepRails Console or via API. This Workflow receives a unique ID (workflow_id). Any application or service across your development, staging, and production environments can then call the DeepRails API, passing this same workflow_id. This ensures uniform AI quality control and behavior correction everywhere the workflow is referenced, simplifying management and ensuring consistency.