I built a system that detects visitor emotions in real-time from mouse telemetry – no surveys, no tracking pixels, zero PII.
The Problem:
Your analytics tell you what happened (user bounced), but not why (they were confused, frustrated, or priced out).
How it works:
– JavaScript captures mouse movements, click patterns, scroll behavior
– Emotional inference engine (Claude Sonnet) analyzes behavioral signatures
– System detects: frustration, confusion, hesitation, confidence, exit intent
– Context-aware interventions deploy in milliseconds
– Feedback loop learns from outcomes
The Stack:
– 20 microservices on EC2 (emotional inference, cross-vertical ML, intervention engine)
– NATS for real-time message streaming
– Supabase for persistence
– Rate-limited and hardened for production
What makes this different:
– No surveys (real-time behavioral inference)
– No PII (emotional states only, no identity tracking)
– Spatial awareness (interventions match page context)
– Self-improving (learns from conversion outcomes)
Demo:
Visit https://sentientiq.ai – you’ll feel it working on you. The interactive demo shows what we detect.
Technical Deep Dive:
Open the browser console on https://sentientiq.ai and watch:
Telemetry stream (mouse movements, clicks, patterns)
Emotion detection (curiosity → overwhelm → confidence)
Intervention deployment (contextual responses)
Full architecture: 20 microservices, NATS streaming, Claude inference (Haiku→Sonnet escalation), rate-limited to 10 Sonnet calls/min/session. Detailed docs coming soon. Happy to answer technical questions here.
Built this solo over 6 months. Nearly died twice. Would love feedback from the HN crowd.
Comments URL: https://news.ycombinator.com/item?id=45573240
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Source: news.ycombinator.com
