EmoSense — Multi-Emotion Recognition System

The Problem

Organizations struggle to operationalize emotion signals from text, voice, and video at enterprise scale while keeping latency acceptable and access tightly governed.

The Solution

I built EmoSense in Flutter with Clean Architecture, BLoC, repository patterns, and enterprise-grade navigation—pairing the client with TensorFlow and Python pipelines for multimodal inference, RBAC‑aware portals, and analytics dashboards tuned for near real‑time monitoring.

Key Outcomes

• Multimodal analysis roadmap spanning text, voice, and video with production-minded UX scaffolding.
• Role‑based admin and employee experiences with analytics oriented toward CX optimization.
• Graduate research project demonstrating CV + ML depth aligned with Nile University work.