This workflow collects article details via a form (e.g., Google Forms). It then uses AI to generate an article outline and the full article content based on the inputs. Finally, it saves the generated content to Google Drive and updates a Google Sheet with links for tracking article progress.
This workflow automates invoice processing by monitoring a Google Drive folder for new PDF invoices. Upon detection of a new PDF, it extracts text content using OCR, then uses AI to parse and extract key invoice details like invoice number, date, and total amount. Finally, it stores the extracted, structured data into a Google Sheet for easy access and reporting.
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 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 enables users to generate high-quality Center of Excellence (COE) blog posts. It takes a topic as input, uses AI to create an outline and then the full blog content, saves it as a Google Doc in Google Drive, and then shares the public link via email to stakeholders.
This workflow monitors a specified Google Sheet for new rows. When a new row is added with a topic, it uses AI to generate a detailed description for that topic. Finally, it updates the original row with the generated description and logs the activity in a separate sheet.
This workflow monitors a Google Drive folder for newly uploaded PDF resumes. It automatically extracts text from the PDF using OCR and then utilizes AI (GPT-4) to extract structured candidate information, such as name, job title, and experience. The extracted data is then used to update an existing row or add a new row in a Google Sheet serving as a candidate database. Finally, the hiring team is notified via Slack with a summary of the new or updated candidate profile.