The research behind SafeFlow.
SafeFlow's AI Assurance methodology is grounded in original research on operational AI reliability and repeated-inference evaluation — a depth-oriented approach to measuring how production AI systems behave under realistic use.
Research that informs our methodology.
The methodology is documented in public preprints, open to scrutiny.
Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing
A framework for estimating empirical failure probability under repeated inference across production-representative prompt distributions. It shows why single-shot benchmarking can understate the operational risk of deployed language models.
Read on arXivEvaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling
An analysis of reliability gaps in safety-critical LLM deployments, demonstrating how repeated prompt sampling surfaces failures that static evaluations miss and how those failures cluster in predictable semantic regions.
Read on arXiv