Gmail and QuickChart integration

🧠 Sentiment Analyzer Google Sheets → OpenAI GPT-4o → QuickChart → Gmail 🚀 What this workflow does Fetches customer reviews from a Google Sheet. Classifies each review as Positive, Neutral or Negative with GPT-4o-mini. Writes the sentiment back to your sheet. Builds a doughnut chart summarising the totals. Emails the chart to your chosen recipient so the whole team stays in the loop. Perfect for support teams, product managers or anyone who wants a zero-code mood ring for their users’ feedback. 🗺️ Node-by-node tour | 🔩 Node | 💡 Purpose | | ------------------------------------------------------- | ---------------------------------------------------------- | | Manual Trigger | Lets you test the workflow on demand. | | Select Google Sheet | Points to the spreadsheet that holds your reviews. | | Loop Over Items | Feeds each row through the analysis routine. | | Sentiment Analysis (LangChain) | Calls GPT-4o-mini and returns only the sentiment category. | | Update Google Sheet | Writes the new Sentiment value into column C. | | Read Data from Google Sheet | Pulls the full sheet again to create a summary. | | Extract Number of Answers per Sentiment (Code node) | Tallies up how many reviews fall into each category. | | Generate QuickChart | Creates a doughnut (or pie) chart as a PNG. | | Send Gmail with Sentiment Chart | Fires the chart off to your inbox. | | (Sticky Notes) | Friendly setup tips scattered around the canvas. | 🛠️ Setup checklist | ✅ Step | Where | | ------------------------------------------------------------------------------------- | -------------------------------- | | Connect Google Sheets → paste your Spreadsheet ID & choose the correct sheet. | All Google Sheets nodes | | Add OpenAI credentials (sk-… key). | Sentiment Analysis node | | Configure Gmail OAuth2 + recipient address. | Gmail node | | Match your sheet columns → “Review title”, “Review text”, empty “Sentiment”. | Google Sheet itself | | (Optional) Switch to gpt-4o for maximum accuracy. | Sentiment Analysis “Model” param | 🏃‍♂️ How to run Drop a few sample reviews into the sheet. Click “Test workflow” on the Manual Trigger. Watch each row march through → sentiment appears in column C. After all rows finish, check your inbox for a fresh chart. ✔️ ✨ Ideas for next level Schedule** the trigger (Cron) to auto-process new reviews daily. Feed the counts to Slack or Discord instead of email. Add a second GPT call to generate a short summary for each review. Happy automating! 🎉

Inspiration & Notes This workflow was born out of a very real problem. While writing a book, I found the process of discovering suitable literary agents and managing outreach to be manual, and surprisingly difficult to scale. Researching agents, checking submission rules, personalizing emails, tracking submissions, and staying organized quickly became a full-time job on its own. So instead of doing it manually, I automated it. I built this entire workflow in 3 days — and the goal of publishing it is to show that you can do the same. With the right structure and intent, complex sales and marketing workflows don’t have to take months to build. Contact & Collaboration If you have questions, business inquiries, or would like help setting up automation workflows, feel free to reach out: 📩 [email protected] I genuinely enjoy designing workflows and automation systems, especially when they support meaningful projects. I work primarily from interest and impact rather than purely financial motivation. Whether I take on a project for FREE or paid for the following reasons: I LOVE setting up workflows and automation. I work for meaningfulness, not for money. I may do the work for free**, depending on how meaningful the project is. If the problem statement matters, the motivation follows. It also depends on the value I bring to the table** -- If I can contribute significant value through system design, I’m more inclined to get involved. If you’re building something thoughtful and need help automating it, I’m always happy to have a conversation. Enjoy~! Overview Automates the end-to-end literary agent outreach pipeline, from data ingestion and eligibility filtering to deep agent research, personalized email generation, submission tracking, and analytics. Architecture The system is organized into four logical domains: The system is modular and is divided into four domains: --> Data Engineering --> Marketing & Research --> Sales (Outreach) --> Data Analysis Each domain operates independently and passes structured data downstream. Data Engineering Purpose: Ingest and normalize agent data from multiple sources into a single source of truth. Inputs Google BigQuery Azure Blob Storage AWS S3 Google Sheets (Optional) HTTP sources Key Steps Scheduled ingestion trigger Merge and normalize heterogeneous data formats (CSV, tables) Deduplication and validation AI-assisted enrichment for missing metadata Append-only writes to a central Google Sheet Output Clean, normalized agent records ready for eligibility evaluation Marketing & Research Purpose: Decide who to contact and how to personalize outreach. Eligibility Evaluation An AI agent evaluates each record against strict rules: Email submissions enabled Not QueryTracker-only or QueryManager-only Genre fit (e.g. Memoir, Spiritual, Self-help, Psychology, Relationships, Family) Outputs send_email (boolean) reason (auditable explanation) Deep Research For eligible agents only: Public research from agency sites, interviews, Manuscript Wish List, and LinkedIn (if public) Extracts: Professional background Editorial interests Genres represented Notable clients/books (if publicly listed) Public statements Source-backed personalization angles Strict Rule: All claims must be explicitly cited; no inference or hallucination is allowed. Sales (Outreach) Purpose: Execute personalized email outreach and maintain clean submission tracking. Steps AI generates agent-specific email copy Copy is normalized for tone and clarity Email is sent (e.g. Gmail) Submission metadata is logged: Submission Completed Submission Timestamp Channel used Result Consistent, traceable outreach with CRM-style hygiene Data Analysis Purpose: Measure pipeline health and outreach effectiveness. Features Append-only decision and submission logs QuickChart visualizations for fast validation (e.g. TRUE vs FALSE completion rates) Optional integration with: Power BI Google Analytics 4 Supports Completion rate analysis Funnel tracking Source/platform performance Decision auditing Design Principles Separation of concerns** (ingestion ≠ decision ≠ outreach ≠ analytics) AI with hard guardrails** (strict schemas, source-only facts) Append-only logging** (analytics-safe, debuggable) Modular & extensible** (plug-and-play data sources) Human-readable + machine-usable outputs** Constraints & Notes Only public, professional information is used No private or speculative data HTTP scraping avoided unless necessary Power BI Embedded is not required Workflow designed and implemented end-to-end in ~3 days Use Cases Marketing Audience discovery Agent segmentation Personalization at scale Campaign readiness Funnel automation Sales Lead qualification Deduplication Outreach execution Status tracking Pipeline hygiene Tech Stack Automation:** n8n AI:** OpenAI (GPT) Scripting:** JavaScript Data Stores:** Google Sheets Email:** Gmail Visualization:** QuickChart BI (optional):** Power BI, Google Analytics 4 Cloud Sources:** AWS S3, Azure Blob, BigQuery Status This workflow is production-ready, modular, and designed for extension into other sales or marketing domains beyond literary outreach.