Automated Customer Email Summarization Workflow

This n8n workflow automates the process of summarizing customer emails received via a webhook. The goal is to extract meaningful insights from email content using natural language processing models, store these summaries for future reference, and alert relevant teams in case of errors.

The workflow begins with a webhook trigger that listens for incoming email data. The email content is then split into manageable segments using a text splitter node. These segments are embedded into vector representations through the Cohere embeddings service, enabling semantic search and comparison.

These embeddings are stored in a Weaviate vector database to facilitate efficient retrieval and similarity checks. When needed, the workflow queries Weaviate to retrieve relevant email data, which is then processed through a language model (via the Anthropic Chat Model) that generates a concise summary of the email content.

A Retrieval-Augmented Generation (RAG) agent integrates all components by handling data, providing context, and generating the summary text. The summarized data, along with status updates, is logged into a Google Sheet for record-keeping. If any errors occur during processing, Slack notifications alert the team immediately.

Practical use cases include customer support, where quick summaries of emails improve response times, and data analysis, where stored summaries can be reviewed to identify common customer issues or feedback patterns.