A local-first behavioral intelligence system. Deterministic pattern detection across human relationships. Cryptographic privacy architecture. No cloud. No account. No server.
GraphMemory builds a living map of your relational world from raw interaction logs. The graph is the product. The journal is the input. The revelation is the output.
GraphMemory sits at the intersection of behavioral pattern recognition, longitudinal relationship indexing, and cognitive preservation. The system generates structured, timestamped relational data — a substrate with applications well beyond its consumer interface.
The dementia application is primary. Longitudinal interaction logs as a cognitive continuity record. A structured life archive — encrypted, exportable, transferable to a trusted custodian via key-sharing. The record persists when memory does not.
The semantic layer maps raw interaction language to sourced clinical frameworks — Bancroft, van der Kolk, George Simon, Cialdini, attachment theory literature. Pattern detection with provenance. Every match traceable to a published source.
The Context Pod is the AI interface layer: a structured plain-text export carrying interaction summaries, pattern breakdowns, and health scores — paste into any large language model for reasoned analysis. The pod travels with the user. It requires no integration.
GRAPHMEMORY CONTEXT POD
Generated: [timestamp]
Key fingerprint: [first-8-chars]****
Interactions: [n] · Health score: [0–100]
Patterns: [weighted breakdown]
Red flags: [auto-detected triggers]
When the same behavioral pattern surfaces across three separate relationships — a parent, an ex, a current colleague — the map stops being data. It becomes a life insight. That moment is what GraphMemory is built to produce.