Customer Sentiment Analysis Workflow

This n8n workflow automates the process of analyzing customer sentiment from online submissions, integrating AI models, vector databases, and real-time notifications to streamline feedback insights. Triggered by a webhook, the workflow begins by receiving customer feedback data, which is then split into manageable chunks for analysis. The content is embedded using Cohere’s API for language understanding, and these embeddings are stored in a Pinecone vector database for efficient similarity searches.

The workflow proceeds with a query to retrieve relevant customer sentiment data, which is processed by an AI language model (using Anthropic’s API) in conjunction with a retrieval-augmented generation (RAG) agent. This setup provides context-aware sentiment insights. The analysis results are logged into a Google Sheet for record-keeping, while any errors encountered trigger an immediate notification in Slack, ensuring prompt attention.

This automation is ideal for businesses that want to monitor and analyze customer feedback efficiently, providing timely insights into customer sentiment, helping improve products, services, or customer experience based on real-time data processing and alerting.