n8n AI Companion Workflow Design -- 2

I’m building a voice-first companion that blends meditation support with nuanced emotional awareness, and I’ve reached the stage where the underlying n8n workflows must be prototyped and stress-tested. Companion interaction is the top priority, yet every flow should be ready to ingest voice streams for downstream emotion analysis so future expansion into full emotion detection is seamless.

The core companion experience needs to cover:
• Conversation abilities
• Personalized recommendations
• Daily activity tracking

Within n8n you’ll wire together speech-to-text, LLM prompts (OpenAI or similar), vector storage for memory, and lightweight analytics that turn usage patterns into actionable suggestions. Each concept should be isolated in its own workflow so performance and UX can be compared quickly. Clear, concise inline documentation inside n8n as well as a short read-me will make hand-off painless.

Deliverables
1. Three to five tested n8n workflows covering the features above
2. Synthetic test data plus logs that demonstrate stability under typical daily load
3. A brief report outlining key findings, improvement ideas, and next steps

A successful hand-off lets me drop these workflows into our existing Docker stack and immediately begin user testing.