Shaterian is a private Living Memory System that transforms voice notes, journals, health observations, and life events into a searchable digital twin. The platform synthesizes fragmented personal data into longitudinal context, enabling users to ask questions and receive reflections about their lives over time. Currently in private beta, the system was developed from the founder's personal archive, including Parkinson's-related health notes, to preserve identity and memory across a full human lifespan.
Funding
Funding not disclosed
Founders
Product
Problem
Personal memories, health observations, journals, photos, and conversations are scattered across disconnected apps and devices, leaving people with fragmented records of their own lives. Health platforms understand health metrics, AI systems understand language, and social platforms understand engagement—but no existing tool understands a human life as a connected whole. As people age or face illness, their life knowledge, wisdom, and identity risk disappearing entirely because current tools preserve documents rather than preserve people.
Solution
Shaterian provides a private Living Memory System that captures life fragments—voice notes, journal entries, health observations, and life events—and structures them into a unified, searchable digital twin. The system creates longitudinal context by connecting these data points across time, allowing users to ask questions about their past and receive reflective, synthesized answers. A user-governed architecture ensures consent and control at every layer, from fragment ingestion through memory structuring to understanding and reflection. The platform's first deployed twin belongs to its founder, built from years of journals, voice memos, Parkinson's notes, and family recollections, demonstrating how personal archives can become a living portrait of a whole life that remains accessible and meaningful over time.
Target Audience
Primary users are individuals facing cognitive decline or chronic illness—such as Parkinson's, dementia, or aging-related conditions—who want to preserve their life story and maintain self-understanding, along with their families and caregivers seeking to keep a loved one's knowledge and identity intact.
Features
- Longitudinal memory synthesis engine that connects fragmented data points into coherent, time-aware life narratives
- Natural language query interface where users can ask questions and receive contextual reflections about past events, health patterns, and personal history
- Voice-first capture system that eliminates typing and administrative overhead for recording memories and observations
- Multi-format ingestion supporting photos, journals, voice memos, health notes, conversations, and family stories in one unified archive
- User-governed data architecture with explicit consent and control mechanisms at each processing stage
- Pattern recognition across health observations, mood, energy levels, and life events to surface correlations that fragmented apps miss
- Life-knowledge preservation framework designed to transfer identity, humor, decisions, and wisdom rather than just documents