This paper presents an offline AI-based log intelligence solution designed for embedded devices, gateways, test systems, servers, and enterprise applications. It addresses the limits of manual log review, especially where logs are fast-growing, unstructured, overwritten quickly, or stored in secure environments that cannot rely on cloud analysis. The engine works by collecting logs, parsing and categorizing them into structured events, correlating behavior chains, detecting anomalies, and producing human-readable summaries locally. It also maps model sizes to deployment environments, from lightweight models for edge devices to larger models for deep diagnostics in labs and servers. The core value is secure, adaptable, read-only analysis that improves root-cause isolation while preserving p

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