AI Memory
Also known as: AI workspace memory, persistent memory.
In plain English
Without memory, every conversation starts from zero — you re-explain your role, your project, and your preferences each time. With memory, the assistant carries forward relevant facts: 'last time we discussed the Q3 forecast, you said you wanted to focus on EMEA'. Memory implementations vary: simple key-value stores, vector databases for semantic recall, or graph databases (knowledge graphs) for structured relationships. Most consumer AI products in 2026 have some form of memory; team-grade memory adds shared workspace memory.
Example
A consultant tells Vincony in week 1: 'I'm working on a healthcare CFO retainer; client is XYZ Hospital; tone should be cautious and citation-heavy.' In week 4 they ask for 'a market trends summary'. Memory recalls the context and the response is tailored without re-explanation.
AI Memory in Vincony
Vincony's Knowledge Graph stores cross-session memory at user and workspace level. Memory entries are user-editable; you can see exactly what's remembered and remove entries.
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