> ## 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 Overview
> The only AI reliability platform that detects hallucinations and automatically corrects them before they reach your users.
DeepRails is a production AI reliability platform built for teams that cannot afford wrong answers. We provide two core services that work together to ensure your AI applications remain accurate, safe, and trustworthy throughout their lifecycle — across healthcare, legal, financial services, education, and any domain where output quality is mission-critical.
Continuously monitor your AI applications for hallucinations, quality regressions, and performance drift in production.
Catch hallucinations in real time and automatically correct them before they reach your users.
Additionally, the intuitive **DeepRails Console** provides a central dashboard to visualize and explore evaluation data, manage defend workflows and monitors, and configure guardrails efficiently.
## The Challenge - Evaluating Model Performance
> **"Lack of evaluations has been a key challenge for deploying to production"**\
> [*- OpenAI, DevDay Conference*](https://youtu.be/XGJNo8TpuVA?feature=shared\&t=1089)
AI systems can generate significantly varied outputs for identical inputs, complicating benchmarks and making consistent evaluation difficult. Current evaluation methods struggle to identify subtle inaccuracies, hallucinations, or early indicators of performance drift, exposing organizations to critical risks.
Additionally, as models evolve, previously reliable methods quickly become obsolete. This requires the need for evaluation tools that keep pace with continuous changes in AI behavior to consistently provide trustworthy insights and guardrails against critical failures.
> **".. don't consider prompts the crown jewels. Evals are the crown jewels"**\
> [*- Jared Friedman, Y Combinator Lightcone Podcast*](https://www.youtube.com/watch?v=DL82mGde6wo\&t=859s)
The best performing prompts are guided by continuous rounds of high quality evaluations.
## What Makes DeepRails Unique
Most AI safety tools stop at detection: they flag problems, block outputs, or log failures for you to handle later. DeepRails goes further. The Defend API corrects hallucinated responses automatically, so your users always get accurate answers.
* **Monitor API**: Real-time detection and observability across your production AI outputs
* **Defend API**: Real-time detection and automatic correction, verified before delivery
In independent benchmarks against AWS Bedrock Guardrails, DeepRails is **45% more accurate on correctness** and **53% more accurate on completeness**. Your AI applications self-heal in production, reducing support tickets and user frustration.
## How DeepRails Works
DeepRails delivers highly performant, research-driven metrics, continuous monitoring capabilities, and real-time guardrails designed specifically for critical AI applications.
Our Guardrails guide both our proprietary Multimodal Partitioned Evaluation (MPE) engine and our one-of-a-kind remediation service for AI hallucinations.
Each guardrail was selected based on years of generative AI experience and rigorous research. The most important metrics were perfected and delivered first, and more are being designed by the DeepRails team continuously.
As part of development, each Guardrail has a Multimodal Partitioned Evaluation prompt individually created and tested. MPE prompts outperform other evaluators by breaking inputs down into granular chunks before evaluating and then aggregating scores.
Learn how DeepRails scores AI outputs using multi-model evaluation, confidence-weighted scoring, and adaptive run modes.
Connect with our team to explore DeepRails' capabilities for your organization.