arXiv.org
An Edge-First Multimodal Asset Management Studio and Retrieval Engine for Heterogeneous AI-Generated Media
AI-generated image collections combine visual content with heterogeneous provenance such as prompts, model parameters, and workflow graphs. Managing these collections locally requires coordinated metadata extraction, responsive browsing, and retrieval without mandatory remote inference. We present Berry AI Studio, an edge-first asset management system built with Vue, Tauri, Rust, SQLite, and ONNX Runtime. Berry integrates format-specific metadata parsing with raw-payload retention, generation-aware cancellation of queued thumbnail work, a persistent multi-tier thumbnail manifest, cursor pagination, and local image-text retrieval. We evaluate these mechanisms in five independent repetitions on a pinned source revision. With 50,000 catalog records, the median warm first-page database call was 285.3 ms; at offset 40,000, keyset pagination reduced median raw-query latency from 57.9 to 0.46 ms. A controlled browser harness recorded a 0.3 ms median-of-run P95 update time during virtualized s
