BriskSnapshot: A Live Demo of Join-Backed Semantic Windows for Streaming AI Pipelines

Abstract

Streaming AI applications need to keep vector state fresh while exposing that state in a form that downstream services can inspect and act on. BriskSnapshot is a live demonstration of a unified semantic-window system implemented over a streaming AI control layer and a vector-stream runtime [7]. The demo follows public vulnerability-intelligence reports as they are normalized, embedded, joined inside a bounded semantic window, and surfaced as snapshot contracts for a provider-agnostic generator service. Unlike a static retrieval demo, BriskSnapshot lets the audience configure the stream, join strategy, retention window, parallelism, and generator backend while seeing how those choices affect runtime evidence and downstream answers. The live walkthrough stays small enough for narration, while a 3,000-record NVD replay and a 20-answer generator run show measured behavior across the vector runtime and contract-to-generation path.