Grounded Personal Memory Retrieval: A Location-Aware AI Assistant with Sentence-Level Verification for Cognitive Support

People with mild cognitive impairment and early Alzheimer’s disease often cannot recall recent personal experiences. Conversational assistants built on large language models could help, but they carry a specific risk for this population: a user with memory impairment cannot check an answer against their own memory, so an unsupported statement may be accepted as a real memory. This paper presents a phone and web memory assistant that retrieves a user’s own recorded experiences by place and by plain-language question while constraining every answer to those records. Photographs, voice notes, and text notes are time-stamped and grouped into named places using geohash prefix search. Questions pass through a retrieval pipeline that refuses before any model call when nothing relevant exists, requires each drafted sentence to cite a retrieved record, and filters every sentence through a verification component that removes uncited, unretrieved, private, medical, or lexically unsupported content. Caregiver access is consent-based, deny-by-default, and visible to the user. We define six measurable grounding requirements and evaluate them on a synthetic persona with 32 records and 48 questions, without patient data. In offline mode, every statement was traceable to a retrieved record, no unsupported statement was produced, 9 of 10 unanswerable questions were refused, and all 28 answerable questions cited a correct record. We discuss the limitations of this evaluation and the work needed before clinical use.