
Real-Time Data Pipeline at APPLSIP
Our Architecture: Databricks + Bedrock + Mistral
Our backend uses a real-time pipeline built on Databricks for orchestration, AWS Bedrock for scalable inference, and Mistral LLMs for multi-language sentiment classification and tagging.
How We Handle Real-Time Feedback Ingestion
We stream reviews, chats, and support data via lightweight event triggers into a message queue, batch them in Databricks, and push structured feedback to downstream APIs within seconds.
LLM vs Traditional NLP: What We Learned
Traditional NLP missed nuance — especially sarcasm and emotional drift. LLMs, with contextual embeddings, drastically improved emotion classification and intent detection, especially in Fintech and app reviews.
Building Emotion Detection with OpenAI/Cohere APIs
We fine-tuned OpenAI and Cohere APIs to recognize emotion layers (e.g. frustration vs disappointment) and assign confidence scores. These results are normalized into sentiment tags for analysis.
API Design for Feedback-as-a-Service
We expose REST APIs for our sentiment engine, allowing any app or support system to send feedback and receive structured JSON output — emotion, category, urgency, and key phrases.
Want to integrate APPLSIP’s real-time sentiment engine into your stack?
📩 Reach out to contact@applsip.com