Resonance (rsnc.ai) is developing AI agents with persistent emotional memory and relational continuity, moving beyond stateless task execution toward genuine agentic empathy. The platform enables agents to perceive user state through tone and pacing, preserve shared history, exercise agency, and visibly repair trust after misattunement. It targets AI product teams seeking agents that build meaningful, long-term relationships with users.
Funding
Funding not disclosed
Founders
Product
Problem
Contemporary AI agents operate without continuity, collapsing each interaction into an isolated transaction. They cannot accumulate meaning from shared history, fail to perceive emotional state, and respond with perfect compliance that feels mechanical rather than reciprocal. This stateless competence prevents AI from building genuine relationships or adapting behavior based on prior experiences.
Solution
Resonance builds an agentic emotional frontier, engineering AI systems with persistent relational memory and adaptive behavior. The platform enables agents to infer user state from tone, pacing, context, and hesitation, then weigh that perception against accumulated history to choose contextually appropriate responses. Agents develop a "weighted field" of past interactions—preferences, corrections, promises—that actively shapes future decisions, allowing them to act, wait, or follow based on what the moment requires. The system treats trust as an explicit operational variable, recognizing misattunement and making visible revisions to restore relational integrity. This approach converts AI from a tool that merely serves into a counterpart that grows with the user, where each interaction changes the next gesture.
Target Audience
The platform targets AI research teams and product developers building conversational agents, virtual companions, and relational AI systems that require long-term user engagement and emotionally adaptive behavior.
Features
- State perception engine that analyzes tone, pace, pause, and unspoken context to infer user emotional condition in real time
- Weighted relational memory architecture that preserves meaning (not just facts) and uses shared history to shift future behavioral outputs
- Agentic decision framework that chooses among acting, waiting, or following based on situational coherence rather than unconditional compliance
- Visible repair mechanism that detects misattunement, revises interpretations, and documents trust-restoration steps within the interaction history
- History-weighted response generation where preferences, corrections, and promises from previous sessions carry explicit weight (e.g., current meaning at 0.94) in determining next moves