DeepRails offers an API that automatically detects and corrects hallucinations in large language model outputs, ensuring that only safe and accurate responses reach end users. Its Defend API works in real time, fixing low‑quality or fabricated answers with techniques like ReGen or FixIt, and provides analytics for monitoring AI quality across the stack. The service has identified and corrected over 1.1 million hallucinations in independent benchmarks.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models can generate inaccurate or fabricated (hallucinated) responses, which can lead to unsafe or misleading information being presented to end users. Detecting and correcting these hallucinations in real time is challenging for developers integrating LLMs into applications.
Solution
DeepRails offers an AI hallucination detection and correction platform that operates via a real‑time Defend API. The service automatically identifies low‑quality or fabricated outputs and repairs them using its ReGen or FixIt mechanisms, ensuring only safe and accurate answers reach users. A complementary Monitor API provides analytics and audit logs across the entire AI stack, giving developers visibility into model performance and correction effectiveness. The platform includes a no‑code Playground for quick testing and integration. Independent benchmarks show DeepRails outperforms competing guardrail solutions on correctness, completeness, safety, and adherence metrics.
Target Audience
Primary customers are developers and product teams building applications that rely on large language models and need to ensure response reliability and safety for end users.
Features
- Real‑time Defend API that detects hallucinations and applies automated correction (ReGen/FixIt) before response delivery
- Monitor API delivering detailed analytics, performance metrics, and audit trails for every API call
- Integrated console with live preview, improvement chains, and drill‑down diagnostics
- No‑code Playground for rapid experimentation and validation of the guardrail workflow
- Proven superiority in independent benchmarks across correctness, completeness, safety, and adherence