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DeepRails specializes in developing research-backed, industry-agnostic Guardrails for retail and enterprise AI applications. We provide two core services to ensure your AI solutions remain effective, safe, and performant throughout their lifecycle.

Monitor

Monitor and analyze AI application performance in production to detect and prevent performance regressions.

Defend

Safeguard production AI applications in real-time with automated guardrails and robust protections.
Additionally, the intuitive DeepRails Console provides a central dashboard to visualize and explore evaluation data, manage defend workflows and monitors, and configure guardrails efficiently.

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The Challenge - Evaluating Model Performance

“Lack of evaluations has been a key challenge for deploying to production”
– OpenAI, DevDay Conference
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
The best performing prompts are guided by continous rounds of high quality evaluations—like the ones that DeepRails provides.

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How DeepRails Works

DeepRails delivers highly performant research-driven metrics, continuous monitoring capabilities, and real-time guardrails designed specifically for mission-critical AI applications. Our Guardrails guide both our proprietary Multimodel 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 are included first, and more are being designed the DeepRails team continuously. As part of development, each Guardrail has a Multimodel 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.

Evaluation Engine

Learn how DeepRails scores AI outputs using multi-model evaluation, confidence-weighted scoring, and adaptive run modes.

Contact Sales

Connect with our team to explore DeepRails’ capabilities for your organization.