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.