Explorations into large language models, computational emotion analysis, real-time ticket ingestion systems, and user behavior prediction metrics.
Analyzing customer sentiment as simple positive or negative is no longer enough. Learn how classifying Joy, Anger, Sadness, Fear, Surprise, and Disgust helps product teams predict churn weeks before it happens.
When a customer writes: 'The interface is gorgeous, but the pricing is way too high', a paragraph-level score averages out to neutral. Discover how our aspect extraction engine separates topics to isolate exact product issues.
Automated support scripts can feel cold and trigger frustration. We explore the customer retention outcomes of using aspect-trained generative models to draft replies that mirror a customer's specific emotional state.
A deep dive into our sub-second latency API architectures, Redis caching layers, and database optimization techniques that process incoming ticket streams under 45ms average latency.
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