Hybrid LLM - Method and System for Confidential and Contextual Use of Large Language Models.

Abstract

This invention discloses an integrated workflow for leveraging external Large Language Models (LLMs) while ensuring that sensitive data remains protected at all times. It addresses the shortcomings of existing Al solutions by combining local anonymization, retrieval-augmented context injection, external LLM invocation, and final consolidation in a novel, modular pipeline. The result is a flexible architecture that preserves data sovereignty, enables continuous learning, and adheres to regulatory requirements (e.g., GDPR, HIPAA). Crucially, the inventive step lies in the coordinated interplay of these components, providing a technical effect that no single module alone can achieve: even though anonymization, retrieval, and external AI calls exist independently, the claimed sequence and synergy ensure that no sensitive data is ever exposed while still yielding robust, context-rich AI outputs. Any variant adhering to the principle of "local data protection + external text generation + local consolidation" is encompassed within the scope of protection. This architecture resolves the known conflict between advanced LLM capability and strict data governance, creating a secure yet high-performance Al pipeline.