ContextHub: A Probabilistic Framework for Universal, Cross-Platform AI Memory and Identity Resolution

The rapid proliferation of specialized Artificial Intelligence (AI) agents has exposed a critical flaw in human-computer interaction: contextual amnesia. Current AI systems operate as isolated Markov decision processes, lacking access to a user's historical state across different platforms, devices, and sessions. This paper introduces ContextHub, a highly performant, universal memory infrastructure layer that utilizes a novel probabilistic identity resolution engine. Rather than relying on deterministic, walled-garden authentication schemas, ContextHub links disparate user interactions through information entropy, Gaussian-boosted semantic similarity, and a cascading 3-Layer context retrieval model driven by client-agnostic hardware anchoring. We detail the system's architecture—leveraging Go, Valkey, and PostgreSQL—and the implementation of TurboQuant, an INT8 scalar quantization mechanism for low-latency vector operations. Furthermore, we analyze the microeconomic viability of the system, demonstrating an infrastructure cost of approximately 3. 00 USD per million requests, and outline a planetary-scale distributed roadmap. Our results indicate that deterministic accounts are no longer a prerequisite for persistent, personalized AI consciousness.