Contextual memory and AI-driven recall
EVA recall trigger, memory insertion, contextual memory lookup
Every assistant engagement flows through a structured interaction. Operator input, system component, and AI assist are explicit at every step.
User provides key terms or triggers to initiate EVA's contextual memory search → System initiates AI's memory recall mechanism → AI searches its memory base using the prompt context
User identifies which part of a process or task EVA is responding to → System returns the specific memory or insight from EVA
User provides labels or metadata to help focus EVA's memory recall on relevant domains → System ranks memories based on relevance, context, and timing
User provides high-level theme or subject matter related to memory insertion → System inserts new data into EVA's memory graph with semantic tagging → AI generates and encodes a new knowledge object into the system
User provides time-based reference (e.g., 'last week,' 'Q4') used to align memory → System aligns memories into a temporal sequence for continuity
User designates the relevance or urgency of a memory for prioritization → System connects new memories to relevant existing nodes by meaning
User provides curated clusters of related ideas or themes → System creates or strengthens conceptual relationships between stored memories → AI builds a visual map of linked concepts and interactions
User provides emotional tone or context that influences memory or communication → System identifies underlying patterns or cognitive similarities
User indicates the strength of a conceptual or relational link → System detects and highlights emerging trends or repeated associations
Each subsystem composes with the others. Browse the platform overview or open an adjacent system to see the bigger picture.
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