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 row is added to a Google Sheet containing a company website URL. It then scrapes the content from that website, uses an AI model to extract key company information such as market, industry, target audience, and value proposition. Finally, the workflow updates the original Google Sheet row with this enriched data.
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 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.
This workflow automatically detects new customer feedback submitted via Google Forms (by monitoring a Google Sheet). It then uses AI to analyze the feedback content and extract impactful testimonial quotes. The extracted quotes are saved to a Google Sheet database, and an email notification is sent to the marketing team with the new testimonial.
This workflow triggers when a new row is added to a Google Sheet, assessing a lead's commitment score. Based on this score, it uses AI to craft a personalized 'warm' or 'cold' email. The appropriate email is then sent via Gmail, and the interaction details are logged in a separate Google Sheet.