System and method for data collection for a personal ai assistant

Abstract

A computer-implemented method for managing household information comprises receiving documents and data from multiple sources including user uploads, API connections, and advisor pre-population, then processing documents through a knowledge processing pipeline comprising classification, extraction, refinement, and synthesis stages executed by specialized LLM-based agents. The method populates domain-specific analytics tables organized by household identifier using a three-phase process: direct field mapping, retrieval-augmented generation enrichment, and cross-document synthesis, wherein each phase executes as separate database transaction providing failure isolation and progressive enrichment. The method supports flexible deployment configurations including business-to-consumer wherein consumers directly populate household data, business-to-business-to-consumer wherein advisors pre-populate client household data before client access, and hybrid configurations enabling transitions between deployment models. The method enables conversational querying via retrieval-augmented generation combining document embeddings and structured analytics, generates comprehensive holistic planning reports with LLM-generated observations and provenance tracking, and supports management of multiple distinct households with independent encryption keys.

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