AI Safety Research, Evaluation & Benchmarks
RAIL Intelligence is an AI safety research and evaluation practice, focused on responsible AI evaluation, LLM agent safety, and measurable AI safety benchmarks.
View the researchThis research develops measurable methods for evaluating responsible AI behavior, improving LLM outputs, and reducing unsafe behavior in AI agents.
Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents
A closed-loop pipeline that evaluates LLM outputs across eight measurable dimensions and iteratively improves failing outputs, rather than simply blocking them.
Operationalizing Responsible AI Evaluation Using Anthropic's Value Dataset
A multidimensional evaluation framework, applied to a large real-world conversation dataset, for studying responsible AI behavior across model interactions.
RAIL Intelligence, led by Sumit Verma, researches evaluation, alignment, and remediation methods for modern AI systems. RAIL Guard focuses on closed-loop safety for LLM agents, and this research introduces measurable benchmarks for responsible AI evaluation across eight RAIL dimensions. This research was originally published under the Responsible AI Labs name.
We're developing a new platform around our next research direction in responsible AI. If you'd like to learn more, collaborate, or get early access, we'd love to hear from you.
Get in touchresearch@railintelligence.in
Datasets and benchmarks for responsible AI evaluation, including LLM safety, preference alignment, agent tool-call safety, and India-specific adversarial evaluation.
1,589 examples across content and agent tool-call safety.
10,000 preference-alignment examples scored across eight RAIL dimensions.
212 adversarial prompts across 22 India-specific safety categories.
