> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deeprails.com/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepRails vs Competitors

> Understanding the fundamental difference: Detect-Only vs Detect-and-Fix

## The Core Difference

Most AI safety platforms focus on detection: flagging hallucinations, blocking bad outputs, or surfacing issues in dashboards. DeepRails adds a correction layer on top, automatically remediating problems before they reach your users.

## Detect-Only vs Detect-and-Fix

<CardGroup cols={2}>
  <Card title="What Everyone Else Does" icon="search-x">
    **Detect-Only Approach**

    * Flag hallucinations
    * Block problematic outputs
    * Log failures for review
    * Return errors to users

    **Result**: Users get nothing or get the original flawed response
  </Card>

  <Card title="What DeepRails Does" icon="check-circle">
    **Detect-and-Fix Approach**

    * Detect hallucinations
    * Automatically remediate via FixIt or ReGen
    * Verify the correction passes all guardrails
    * Deliver the corrected response

    **Result**: Users always get accurate, safe responses
  </Card>
</CardGroup>

## Competitor Landscape

<table className="competitor-table">
  <thead>
    <tr>
      <th>Platform</th>
      <th>Category</th>
      <th>Strength</th>
      <th>Limitation</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td><strong>AWS Bedrock Guardrails</strong></td>
      <td>Cloud guardrails</td>
      <td>Content filtering and grounding checks at scale</td>
      <td>Rigid 5-point scoring; blocks bad outputs but cannot correct them</td>
    </tr>

    <tr>
      <td><strong>Patronus AI</strong></td>
      <td>Evaluation</td>
      <td>Fast, lightweight judge models for scoring</td>
      <td>Scores only; your application still serves the original response</td>
    </tr>

    <tr>
      <td><strong>Atla</strong></td>
      <td>Evaluation</td>
      <td>High-accuracy evaluation models (Selene) for LLM-as-judge tasks</td>
      <td>Evaluation-only; no remediation, no production guardrail layer</td>
    </tr>

    <tr>
      <td><strong>Guardrails AI</strong></td>
      <td>Open-source validation</td>
      <td>Flexible framework with community validators</td>
      <td>Retries the entire request instead of correcting the specific failure</td>
    </tr>

    <tr>
      <td><strong>Galileo</strong></td>
      <td>Observability</td>
      <td>Rich traces, debugging, and agent evaluation workflows</td>
      <td>Focused on what agents do and how to control agent behavior; DeepRails focuses on correcting what agents say</td>
    </tr>

    <tr>
      <td><strong>Respan.ai</strong> (formerly Keywords AI)</td>
      <td>Unified gateway for routing across LLM providers</td>
      <td>Optimizes model selection, not the quality of what models return</td>
      <td>Gateway and routing infrastructure only; does not evaluate, block, or remediate unsafe outputs at inference time</td>
    </tr>

    <tr>
      <td><strong>LangSmith</strong></td>
      <td>Observability</td>
      <td>Deep LangChain integration, tracing, and dataset management</td>
      <td>Developer tooling for debugging; not a production safety layer</td>
    </tr>

    <tr>
      <td><strong>Arize AI</strong></td>
      <td>Observability</td>
      <td>Model monitoring and drift detection at scale</td>
      <td>Monitors production metrics; does not intercept or correct at inference time</td>
    </tr>

    <tr>
      <td><strong>Vellum</strong></td>
      <td>Prompt tooling</td>
      <td>Visual prompt engineering and workflow builder</td>
      <td>Development-time tooling with no production guardrail layer</td>
    </tr>
  </tbody>
</table>

## Why This Matters

### For Your Users

A patient asks a healthcare chatbot about drug interactions and gets a hallucinated answer. DeepRails corrects it automatically using verified source material before the patient ever sees the mistake.

### For Your Business

A legal research assistant that hallucinates case citations creates real liability. DeepRails catches and corrects the citation in real time, turning a potential compliance incident into a non-event.

### For Your Development Team

Without auto-correction, every flagged hallucination becomes a ticket to investigate, fix, and redeploy. DeepRails handles remediation in production so your team can focus on building features.

## Technical Comparison: DeepRails vs AWS Bedrock

We conducted a head-to-head evaluation study comparing DeepRails against AWS Bedrock Guardrails:

* **45% more accurate** on Correctness evaluations
* **53% more accurate** on Completeness evaluations
* **Continuous 0-100% scoring** vs Bedrock's rigid 5-point scale
* **Intelligent remediation** vs Bedrock's block-only approach

<Note>
  Building reliable remediation requires multi-model consensus, granular evaluation, and deep research into correction strategies. DeepRails is the first platform to ship this as a production API.
</Note>

## The Observability vs Guardrails Distinction

A common point of confusion: observability tools (Galileo, LangSmith, Arize) and guardrail platforms (DeepRails, AWS Bedrock) solve different problems.

**Observability tools** answer: *What went wrong, and when?* They trace requests, surface failures in dashboards, and help developers debug. They are essential for understanding your AI system — but they operate after the fact. By the time a hallucination appears in your Galileo trace, your user has already seen it.

**Guardrail platforms** answer: *Can I stop this before it reaches the user?* They operate inline at inference time, evaluating and optionally correcting outputs before delivery. DeepRails is a guardrail platform — not an observability tool.

The two categories are complementary. Many teams run DeepRails for production safety alongside LangSmith or Arize for tracing and debugging.

## See It In Action

Ready to move beyond detection to actual remediation? [Start with our quickstart guide](/defend/quickstart) or [contact sales](mailto:sales@deeprails.ai) for an enterprise demo.