Memoria: The Neural Anchor

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Memoria — Overview

The Spark
(summary converted to Markdown paragraph)

The Science
We model the user's Cognitive Engagement Score (E_c) as:

$$ E_c = \frac{\sum_{i=1}^{n} \bigl(w_r \cdot R_i + w_e \cdot \varepsilon_i\bigr)}{T_{\text{response}}} $$

Where:

• (R_i) — accuracy of a recall event
  
• (\varepsilon_i) — emotional sentiment score (audio)
  
• (T_{\text{response}}) — response latency normalized to baseline

How We Built It

• Voice: ElevenLabs (streaming, low-latency)
  
• Brain: Google Vertex AI (Gemini Pro, large context)
  
• Infrastructure: real-time transcription → cognitive marker analysis → model-driven voice responses

Challenges

• Latency vs. empathy (optimized via low-latency WebSocket)
  
• Emotional nuance (prompt tuning to validate, not correct)

Built With

• ai
• api
• elevenlabs
• elevenlabs-react-sdk-(@elevenlabs/react)-architecture:-websocket-streaming-for-real-time
• firestore
• functions
• gemini
• gemini-pro
• google-cloud-functions
• google-firestore-apis:-elevenlabs-conversational-agents-api
• javascript
• node.js
• node.js-frameworks:-react-(vite)
• react
• tailwindcss
• tailwindcss-cloud-services:-google-cloud-vertex-ai-(gemini-pro)
• typescript
• vertex
• vite