This workflow triggers when new customer feedback is added as a row in Google Sheets. It then uses an AI model to classify the sentiment of the feedback as positive, neutral, or negative. Finally, the classified sentiment is updated back into the same Google Sheet.
This workflow is triggered when user feedback is updated in a Notion database. It uses an AI to perform sentiment analysis (Positive, Neutral, Negative) and determine if the feedback matches an existing insight or requires a new one. The workflow then updates the Notion database, linking feedback to insights and marking it as processed.
This workflow automatically triggers when a new product review is added to a specified Google Sheet. It then uses AI to analyze the sentiment of the review (positive, neutral, or negative) and writes the sentiment back into a designated column in the same Google Sheet.
This workflow triggers when a new email is received in Gmail. It uses AI to analyze the email's subject and body, then determines a category and request type. It retrieves existing entries from a Google Sheet. If a matching category and request are found, it increments the count for that specific row; otherwise, it adds a new row to the sheet.
This workflow is triggered when a new row is added to a Google Sheet containing a document link. It processes the document (e.g., a PDF resume) by extracting its text content. Using prompts fetched from another Google Sheet, an AI extracts specific fields of information. Finally, the extracted data is updated back into the original Google Sheet row.
This workflow automatically monitors your Gmail inbox for new inquiries. It classifies incoming emails using AI, retrieves relevant context from a Google Sheet acting as an FAQ database, and generates a professional, context-aware reply for valid inquiries. Finally, it sends the AI-generated response via Gmail and logs all interaction details (original email, AI response, timestamp, and sender) to a Google Sheet for tracking.
This workflow automatically generates detailed product comparison content. It can be triggered manually or when a new row is added to Google Sheets, taking two product names as input. An AI model then generates a compelling title, a feature-by-feature comparison, use-case recommendations, and an FAQ section. Finally, the generated content is saved into a Google Sheet for review or publishing.