Workflow Templates

Who this is for This workflow is designed for teams that collect feedback or survey responses via Jotform and want to automatically: Analyze sentiment (positive, neutral, negative) of each response. Extract key topics and keywords from qualitative text. Generate AI summaries and structured insights. Store results in Google Sheets and n8n DataTables for easy reporting and analysis. Use Cases Customer experience analysis Market research & survey analysis Product feedback clustering Support ticket prioritization AI-powered blog or insight generation from feedback What this workflow does This n8n automation connects Jotform, Google Gemini, and Google Sheets to turn raw responses into structured insights with sentiment, topics, and keywords. Pipeline Overview Jotform → Webhook → Gemini (Topics + Keywords) → Gemini (Sentiment) → Output Parser → Merge → Google Sheets Jotform Trigger Captures each new submission from your Jotform (e.g., a feedback or survey form). Extracts raw fields ($json.body.pretty) such as name, email, and response text. Format Form Data (Code Node) Converts the Jotform JSON structure into a clean string for AI input. Ensures the text is readable and consistent for Gemini. Topics & Keyword Extraction (Google Gemini + Output Parser) Goal: Identify the main themes and important keywords from responses. { "topics": [ { "topic": "Product Features", "summary": "Users request more automation templates.", "keywords": ["AI templates", "automation", "workflow"], "sentiment": "positive", "importance_score": 0.87 } ], "global_keywords": ["AI automation", "developer tools"], "insights": ["Developers desire more creative, ready-to-use AI templates."], "generated_at": "2025-10-08T10:30:00Z" } Sentiment Analyzer (Google Gemini + Output Parser) Goal: Evaluate overall emotional tone and priority. { "customer_name": "Ranjan Dailata", "customer_email": "ranjancse@gmail.com", "feedback_text": "Please build more interesting AI automation templates.", "sentiment": "positive", "confidence_score": 0.92, "key_phrases": ["AI automation templates", "developer enablement"], "summary": "Customer requests more AI automation templates to boost developer productivity.", "alert_priority": "medium", "timestamp": "2025-10-08T10:30:00Z" } Merge + Aggregate Combines the topic/keyword extraction and sentiment output into a single structured dataset. Aggregates both results for unified reporting. Persist Results (Google Sheets) Writes combined output into your connected Google Sheet. Two columns recommended: feedback_analysis → Sentiment + Summary JSON topics_keywords → Extracted Topics + Keywords JSON Enables easy visualization, filtering, and reporting. Visualization (Optional) Add Sticky Notes or a logo image node in your workflow to: Visually describe sections (e.g., “Sentiment Analysis”, “Topic Extraction”). Embed brand logo: Example AI Output (Combined) { "feedback_analysis": { "customer_name": "Ranjan Dailata", "sentiment": "positive", "summary": "User appreciates current templates and suggests building more advanced AI automations.", "key_phrases": ["AI automation", "developer templates"] }, "topics_keywords": { "topics": [ { "topic": "AI Template Expansion", "keywords": ["AI automation", "workflow templates"], "sentiment": "positive", "importance_score": 0.9 } ], "global_keywords": ["automation", "AI development"] } } Setup Instructions Pre-requisite If you are new to Jotform, Please do signup using Jotform Signup For the purpose of demonstation, we are considering the Jotforms Prebuilt New Customer Registration Form as a example. However, you are free to consider for any of the form submissions. Step 0: Local n8n (Optional) If using local n8n, set up ngrok: ngrok http 5678 Use the generated public URL as your Webhook URL base for Jotform integration. Step 1: Configure the Webhook Copy the Webhook URL generated by n8n (e.g., /webhook-test/f3c34cda-d603-4923-883b-500576200322). You can copy the URL by double clicking on the Webhook node. Make sure to replace the base url with the above Step 0, if you are running the workflow from your local machine. In Jotform, go to your form → Settings → Integrations → Webhooks → paste this URL. Now, every new form submission will trigger the n8n workflow. Step 2: Connect Google Gemini Create a Google Gemini API Credential in n8n. Select the model models/gemini-2.0-flash-exp. Step 3: Create Data Storage Create a DataTable named JotformFeedbackInsights with columns: feedback_analysis (string) topics_keywords (string) Step 4: Connect Google Sheets Add credentials under Google Sheets OAuth2. Link to your feedback tracking sheet. Step 5: Test the Workflow Submit a form via Jotform. Check results: AI nodes return structured JSON. Google Sheet updates with new records. Customization Tips Change the Prompt You can modify the topic extraction prompt to highlight specific themes: You are a research analyst. Extract main topics, keywords, and actionable insights from this feedback: {{ $json.body }} Extend the Output Schema Add more fields like: { "suggested_blog_title": "", "tone": "", "recommendations": [] } Then update your DataTable or Sheets schema accordingly. Integration Ideas Send sentiment alerts to Slack for high-priority feedback. Push insights into Notion, Airtable, or HubSpot. Generate weekly reports summarizing trends across all submissions. Summary This workflow turns raw Jotform submissions into actionable insights using Google Gemini AI — extracting topics, keywords, and sentiment while automatically logging everything to Google Sheets.

Streamline M&A due diligence with AI. This n8n workflow automatically parses financial documents using LlamaIndex, embeds data into Pinecone, and generates comprehensive, AI-driven reports with GPT-5-mini, saving hours of manual review and ensuring consistent, data-backed insights. Why Use This Workflow? Time Savings: Reduces manual document review and report generation from days to minutes. Cost Reduction: Minimizes reliance on expensive human analysts for initial data extraction and summary. Error Prevention: AI-driven analysis ensures consistent data extraction, reducing human error and oversight. Scalability: Effortlessly processes multiple documents and deals in parallel, scaling with your business needs. Ideal For Investment Analysts & Private Equity Firms:** Quickly evaluate target companies by automating the extraction of key financials, risks, and business models from deal documents. M&A Advisors:** Conduct preliminary due diligence efficiently, generating comprehensive overview reports for clients without extensive manual effort. Financial Professionals:** Accelerate research and analysis of company filings, investor presentations, and market reports for critical decision-making. How It Works Trigger: A webhook receives multiple due diligence documents (PDFs, DOCX, XLSX) along with associated metadata. Document Processing & Cache Check: Files are split individually. The workflow first checks Pinecone to see if the deal's documents have been processed before (cache hit). If so, it skips parsing and embedding. Data Extraction (LlamaIndex): For new deals, each document is sent to LlamaIndex for advanced parsing, extracting structured text content. Vectorization & Storage: The parsed text is then converted into numerical vector embeddings using OpenAI and stored in Pinecone, our vector database, with relevant metadata. AI-Powered Analysis (Langchain Agent): An n8n Langchain Agent, acting as a "Senior Investment Analyst," leverages GPT-5-mini to query Pinecone multiple times for specific information (e.g., company profile, financials, risks, business model). It synthesizes these findings into a structured JSON output. Report Generation: The structured AI output is transformed into an HTML report, then converted into a professional PDF document. Secure Storage & Delivery: The final PDF due diligence report is uploaded to an S3 bucket, and a public URL is returned via the initial webhook, providing instant access. Setup Guide Prerequisites | Requirement | Type | Purpose | | :---------- | :--- | :------ | | n8n instance | Essential | Workflow execution platform | | LlamaIndex API Key | Essential | For robust document parsing and text extraction | | OpenAI API Key | Essential | For creating text embeddings and powering the GPT-5-mini AI agent | | Pinecone API Key | Essential | For storing and retrieving vector embeddings | | AWS S3 Account | Essential | For secure storage of generated PDF reports | Installation Steps Import the JSON file to your n8n instance. Configure credentials: LlamaIndex: Create an "HTTP Header Auth" credential with x-api-key in the header and your LlamaIndex API key as the value. OpenAI: Create an "OpenAI API" credential with your OpenAI API key. Ensure the credential name is "Sumopod" or update the workflow nodes accordingly. Pinecone: Create a "Pinecone API" credential with your Pinecone API key and environment. Ensure the credential name is "w3khmuhtadin" or update the workflow nodes accordingly. AWS S3: Create an "AWS S3" credential with your Access Key ID and Secret Access Key. Update environment-specific values: In the "Upload to S3" node, ensure the bucketName is set to your desired S3 bucket. In the "Create Public URL" node, update the baseUrl variable to match your S3 bucket's public access URL or CDN if applicable (e.g., https://your-s3-bucket-name.s3.amazonaws.com). Customize settings: Review the prompt in the "Analyze" (Langchain Agent) node to adjust the AI's persona or required queries if needed. Test execution: Send sample documents (PDF, DOCX, XLSX) to the webhook URL (/webhook/dd-ai) to verify all connections and processing steps work as expected. Technical Details Core Nodes | Node | Purpose | Key Configuration | | :------------------------------ | :--------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------- | | Webhook | Initiates workflow with document uploads | Path: dd-ai, HTTP Method: POST | | Split Multi-File (Code) | Splits binary files, generates unique deal ID | Parses filenames from body or binary, creates dealId from sorted names. | | Parse Document via LlamaIndex | Extracts structured text from various document types | URL: https://api.cloud.llamaindex.ai/api/v1/parsing/upload, Authentication: HTTP Header Auth with x-api-key. | | Monitor Document Processing | Polls LlamaIndex for parsing status | URL: https://api.cloud.llamaindex.ai/api/v1/parsing/job/{{ $json.id }}, Authentication: HTTP Header Auth. | | Insert to Pinecone | Stores vector embeddings in Pinecone | Mode: insert, Pinecone Index: poc, Pinecone Namespace: dealId. | | Data Retrieval (Pinecone) | Enables AI agent to search due diligence documents | Mode: retrieve-as-tool, Pinecone Index: poc, Pinecone Namespace: {{ $json.dealId }}, topK: 100. | | Analyze (Langchain Agent) | Orchestrates AI analysis using specific queries | Prompt Type: define, detailed role and 6 mandatory Pinecone queries, Model: gpt-5-mini, Output Parser: Parser. | | Generate PDF (Puppeteer) | Converts HTML report to a professional PDF | Script Code: await $page.pdf(...) with A4 format, margins, and 60s timeout. | | Upload to S3 | Stores final PDF reports securely | Bucket Name: poc, File Name: {{ $json.fileName }}, Credentials: AWS S3. | | If (Check Namespace Exists) | Implements caching logic | Checks stats.namespaces[dealId].vectorCount > 0 to determine cache hit/miss. | Workflow Logic The workflow begins by accepting multiple files via a webhook. It intelligently checks if the specific "deal" (identified by a unique ID generated from filenames) has already had its documents processed and embedded in Pinecone. This cache mechanism prevents redundant processing, saving time and API costs. If a cache miss occurs, documents are parsed by LlamaIndex, their content vectorized by OpenAI, and stored in a Pinecone namespace unique to the deal. For analysis, a Langchain Agent, powered by GPT-5-mini, is instructed with a specific persona and a mandatory sequence of Pinecone queries (e.g., company overview, financials, risks). It uses the Data Retrieval tool to interact with Pinecone, synthesizing information from the stored embeddings. The AI's output is then structured by a dedicated parser, transformed into a human-readable HTML report, and converted into a PDF. Finally, this comprehensive report is uploaded to AWS S3, and a public access URL is provided as a response. Customization Options Basic Adjustments: AI Prompt Refinement:** Modify the Prompt field in the "Analyze" (Langchain Agent) node to adjust the AI's persona, introduce new mandatory queries, or change reporting style. Output Schema:** Update the JSON schema in the "Parser" (Langchain Output Parser Structured) node to include additional fields or change the structure of the AI's output. Advanced Enhancements: Integration with CRM/Dataroom:** Add nodes to automatically fetch documents from or update status in a CRM (e.g., Salesforce, HubSpot) or a virtual data room (e.g., CapLinked, Datasite). Conditional Analysis:** Implement logic to trigger different analysis paths or generate different report sections based on document content or deal parameters. Notification System:** Integrate with Slack, Microsoft Teams, or email to send notifications upon report generation or specific risk identification. Use Case Examples Scenario 1: Private Equity Firm Evaluating a Target Company Challenge: A private equity firm receives dozens of due diligence documents (financials, CIM, management presentations) for a potential acquisition, needing a rapid initial assessment. Solution: The workflow ingests all documents, automatically parses them, and an AI agent synthesizes key company information, financial summaries (revenue history, margins), and identified risks into a structured report within minutes. Result: The firm's analysts gain an immediate, comprehensive overview, enabling faster screening and more focused deep-dive questions, significantly accelerating the deal cycle. Scenario 2: M&A Advisor Conducting Preliminary Due Diligence Challenge: An M&A advisory firm needs to provide clients with a quick, consistent, and standardized preliminary due diligence report across multiple prospects. Solution: Advisors upload relevant prospect documents to the workflow. The AI-powered system automatically extracts core business model details, investment thesis highlights, and customer concentration analysis, along with key financials. Result: The firm can generate standardized, high-quality preliminary reports efficiently, ensuring consistency across all client engagements and freeing up senior staff for strategic analysis. Created by: Khmuhtadin Category: AI | Tags: Due Diligence, AI, Automation, M&A, LlamaIndex, Pinecone, GPT-5-mini, Document Processing Need custom workflows? Contact us Connect with the creator: Portfolio • Workflows • LinkedIn • Medium • Threads

Who Is This For? This workflow is designed for educators, universities, examination departments, and EdTech institutions that need a faster, smarter, and standardized way to prepare exam question papers. What Problem Does This Solve? Creating balanced, outcome-based question papers can take hours or even days of manual effort. Faculty often struggle to: Ensure syllabus coverage across units Maintain Bloom’s Taxonomy alignment Keep a consistent difficulty balance Format papers in institution-specific templates How it works This workflow automatically generates an exam question paper based on syllabus topics submitted via a form and sends it to the entered email address. Here’s the flow in simple steps: Form Submission – A student or faculty fills out a form with subject code, syllabus topics, and their email. AI Question Generation – The workflow passes the syllabus to AI agents (Part A with 2 Marks, Part B with 13 Marks, and Part C with 14 Marks) to create question sets. The marks and the no. of question generated can be customized according to the convenience. Merging Questions – All AI-generated questions are combined into a single structured document. Format into HTML – The questions are formatted into a clean HTML exam paper (can also be extended to PDF). Send by Email– The formatted exam paper is sent to the user’s email (with option to CC/BCC). Set up steps Connect Accounts Connect your OpenAI (or LLM) credentials for AI-powered question generation. Connect your Gmail (or preferred email service) to send emails. Prepare Form Create an n8n form trigger with required fields: Subject with Code Syllabus for Unit 1, 2, 3… Email to receive the paper Customize Question Generation Modify the AI prompts for Parts A, B, and C to fit your syllabus style (e.g., 2-mark, 13-mark, 14-mark). Format the Exam Paper Adjust the HTML template to match your institution’s exam paper layout. Test & Deploy Submit a test form entry. Check the received email to ensure formatting looks good. Deploy the workflow to production for real usage. Need help customizing? ✉️ Contact Me 💼 LinkedIn

Description This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI-powered diagnostics, and alerts your team only when real issues occur. Notion holds all data quality rules (source, field, condition, severity). n8n reads them on schedule, converts them into live SQL queries, and aggregates anomalies into a global run summary. The workflow then scores data health, creates a Notion run record, optionally opens a Jira issue, and sends a Slack/email alert including AI-generated root cause & recommended fixes. Target users Perfect for: DataOps Analytics Product Data BI Compliance ETL/ELT pipelines Platform reliability teams. Workflow steps How it works 1) Notion → Rules Database Each entry defines a check (table, field, condition, severity). 2) n8n → Dynamic Query Execution Rules are converted into SQL and checked automatically. 3) Summary Engine Aggregates anomalies, computes data quality score. 4) AI Diagnostic Layer Root cause analysis + recommended fix plan. 5) Incident Handling Notion Run Page + optional Slack/Email/Jira escalation. Silent exit when no anomaly = zero noise. Setup Instructions Create two Notion databases: Data Quality Rules → source / field / rule / severity / owner Data Quality Runs → run_id / timestamp / score / anomalies / trend / AI summary/recommendation Connect SQL database (Postgres / Supabase / Redshift etc.) Add OpenAI credentials for AI analysis Connect Slack + Gmail + Jira for incident alerts Set your execution schedule (daily/weekly) Expected outcomes Fully automated, rule-based data quality monitoring with minimal maintenance and zero manual checking. When everything is healthy, runs remain silent. When data breaks, the team is notified instantly: with context, root cause insight, and a structured remediation output. Tutorial video Watch the Youtube Tutorial video About me : I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Who's it for This template is for Customer Success and Sales teams who use HubSpot. It automates the critical handoff from sales to success, ensuring every new customer gets a fast, personalized welcome. It's perfect for anyone looking to standardize their onboarding process, save time on manual tasks, and improve the new customer experience using AI. What it does This workflow triggers when a deal's "Is closed won" property is set to True in HubSpot. It assigns a Customer Success Manager (CSM) by querying an n8n Data Table to find the 'least busy' CSM (based on a deal count) and fetches the deal's details to find all associated contacts. It then loops to identify the "Champion" contact by checking their "Buying Role" (hs_buying_role). An AI agent (in the AI: Write Welcome Email node) generates a personalized welcome email, which is converted to HTML and sent via Gmail. Finally, the workflow updates the Champion's contact record in HubSpot and updates the CSM's deal count in the Data Table to keep the logic in sync. How to set up Create and Populate Data Table: This template requires an n8n Data Table to manage CSM assignments. Create a Data Table named csm_assignments. Add two columns: csm_id (String) and deal_count (Number). Add one row for each CSM with their HubSpot Owner ID and a starting deal_count of 0. Link Data Table Nodes: Open the Get CSM List and Increment CSM Deal Count nodes and select the csm_assignments table you just created from the Table dropdown. Configure Variables: In the Configure Template Variables node, you must set your sender info (company_name, sender_name, and sender_email). Customize AI Prompt: In the AI: Write Welcome Email node, update the placeholder [Link to Your Video] and [Link to Your Help Doc] links with your own URLs. Check HubSpot Property: This workflow assumes you use the "Buying Role" (hs_buying_role) contact property to identify your "Champion". If you use a different property, you must update the HubSpot: Get Contact Details and If Role is 'Champion' nodes. Requirements Access to n8n Data Tables. HubSpot (Developer API):** A credential for the Trigger: Deal Is 'Closed Won' node. HubSpot (OAuth2):** A credential for all other HubSpot nodes (Get Deal Details, Get Contact Details, Assign Contact Owner). AI Credentials:** (e.g., OpenAI) Credentials for the AI Model node (the node connected to AI: Write Welcome Email). Email Credentials:** (e.g., Gmail) Credentials for the Gmail: Send Welcome Email node. How to customize the workflow You can easily customize this workflow to send different emails based on deal properties. Add an If node after the HubSpot: Get Deal Details node to check for the deal's value, product line, or region. Based on these properties, you can route the flow to different AI: Write Welcome Email nodes with unique prompts. For example, you could check the contact's 'industry' or 'company size' to send them links to different, more relevant 'Getting Started' videos and documentation.

How It Works This workflow automates insurance claims processing by deploying specialized AI agents to analyze actuarial data, draft claim memos, and perform risk assessments. Designed for insurance adjusters, underwriters, and claims managers handling high claim volumes, it solves the bottleneck of manual claim review that delays settlements and increases operational costs. The system ingests new claims data via scheduled triggers, then routes information to an actuarial analysis agent that calculates loss ratios and risk scores. A memo writer agent generates detailed claim summaries with recommendations, while a risk assessment agent evaluates fraud indicators and coverage implications. An orchestrator agent coordinates these specialists, ensuring consistent analysis standards. Final reports are automatically distributed via email to product teams and Slack notifications to risk management, creating transparent workflows while reducing claim processing time from days to hours with standardized, comprehensive evaluations. Setup Steps Configure claims database API credentials in "Fetch New Claims Data" node Input NVIDIA API key for all OpenAI Model nodes Add OpenAI API key in Orchestrator Agent configuration Set up Calculator Tool parameters for premium adjustment calculations Configure Gmail credentials and recipient addresses for product team Connect Slack workspace and specify risk team channel for alerts Prerequisites NVIDIA API access, OpenAI API key, claims management system API Use Cases Auto insurance claim triage, property damage assessment automation Customization Adjust risk scoring thresholds, add industry-specific analysis criteria Benefits Reduces claim processing time by 85%, ensures consistent evaluation standards

Stop watching long videos, start listening to concise summaries. This workflow transforms any YouTube video URL sent via Telegram into a high-quality, spoken audio summary (MP3) and a structured text overview. It acts as your personal AI research assistant, turning lengthy content into bite-sized audio files that you can consume on the go. It leverages Decodo for robust transcript extraction, OpenAI for intelligent summarization, and for realistic text-to-speech generation. ✨ Features Telegram-First Interface:** Send links and receive audio directly in your chat app. Smart Validation:** Automatically checks if the link is a valid YouTube URL before processing to save API credits. Multi-Language Support:** Easily configure the output language (English, Spanish, German, etc.) via a simple Config node. The AI will translate and speak in this language. Robust Error Handling:** Gracefully handles videos with no captions/transcripts by notifying the user instead of breaking the workflow. Structured Data Extraction:** Uses AI to extract the Genre, Title, and Summary alongside the audio file. ⚙️ How it Works Trigger: You send a YouTube URL to your Telegram Bot. Validate: The workflow checks the URL pattern using Regex. Extract: Decodo scrapes the video page to retrieve the full transcript JSON. Process: A Code node flattens the complex JSON into a readable text format. Summarize: OpenAI (gpt-4o-mini) analyzes the text and writes a script optimized for listening. Speak: OpenAI converts the script into a high-definition MP3 file. Deliver: The bot replies with the Audio File and a formatted text summary including the genre tags and original link. 📥 Decodo Node Installation The Decodo node is used in this workflow for fetching the YouTube Transcript. Find the Node: Click the + button in your n8n canvas. Search: Search for the Decodo node and select it. Credentials: When configuring the first Decodo node, use your API key (obtained with the 80% discount coupon). Setup: Open the Decodo (Fetch YouTube Transcript) node to ensure it is correctly targeting the YouTube service. 🎁 Exclusive Deal for n8n Users To run this workflow, you require a robust scraping provider. We have secured a massive discount for Decodo users: Get 80% OFF the 23k Advanced Scraping API plan. Coupon Code: ATTAN8N Sign Up Here: Claim 80% Discount on Decodo ➕ How to Adapt the Template This workflow is highly flexible and can be modified for various content tasks: Change AI Model:* Easily swap the *OpenAI Chat Model* node with an *OpenAI* or *Anthropic (Claude)** node without altering the core logic. Create Long-Form Drafts:** Modify the AI System Prompt to generate a full 1,000-word blog post draft or a set of social media updates instead of a short audio script. Change Destination:* Replace the *Telegram* nodes with *Slack, **Microsoft Teams, Email (Gmail/SMTP), or Discord to deliver the audio and summary to your preferred channel. Create an Archive:* Connect the successful output to a *Google Sheets* or *Airtable** node to keep a searchable archive of every video summary created.

Customer Support AI Agent for Gmail This n8n template demonstrates how to build an AI-powered customer support workflow that automatically handles incoming Gmail messages, classifies them, finds answers from your knowledge base, and sends a personalized reply. Who’s it for SaaS founders or teams who want to automate customer support. Freelancers and solopreneurs who receive repetitive customer queries. Companies that want to reduce manual email triage and improve response times. How it works / What it does Trigger: A new email arrives in Gmail. Classification: The workflow uses a text classifier to decide whether the email is customer support-related or not. If not, it’s ignored. If yes, it proceeds. AI Agent: Queries a knowledge base (vector database with OpenAI embeddings). Retrieves the most relevant answer. Drafts a reply using AI (OpenAI or Google Gemini model). Post-processing: Labels the email in Gmail for organization. Sends a reply automatically. This ensures that your customers get timely, relevant responses without manual intervention. How to set up Import this template into your n8n account. Connect your Gmail account in the Gmail Trigger, Label, and Reply nodes. Connect your AI model provider (OpenAI or Google Gemini). Configure the knowledge base embeddings (upload your docs/FAQ into the vector database). Activate the workflow — and your AI customer support agent is live! Requirements n8n account. Gmail account (with API access enabled). OpenAI or Google Gemini account for LLM and embeddings. Knowledge base data (FAQ, documentation, or past tickets). Google Drive account for auto update your vector database(with API access enabled). How to customize the workflow Knowledge Base**: Replace or expand with your own company docs, FAQs, or past conversations. Classification Rules**: Train or adjust the classifier to handle more categories (e.g., Sales, Partnership, Technical Support). Reply Style**: Customize AI prompts for tone — professional, casual, or friendly. Labels**: Change Gmail labels to match your workflow (e.g., “Support,” “Sales,” “Priority”). Multi-language**: Add translation steps if your customers speak different languages. This template saves you hours of manual email triage and ensures your customers always get quick, accurate responses.

How It Works This workflow automates enterprise budget monitoring and cost optimization using Anthropic Claude as the core AI engine across multiple specialist agents. It targets finance teams, operations managers, and CFOs managing complex multi-department budgets where manual tracking leads to delayed decisions and cost overruns. The workflow triggers on schedule, generates metrics data, and routes it through a Cost Intelligence Agent that classifies budget status (Critical, Warning, Review, Feedback). Each path activates specialist agents—Budget Alert, Routing Recommendation, and Cost Projection—coordinated by an Optimization Coordinator. Results are routed by action type: urgent alerts fire via Slack, executive summaries deliver via email, and all optimization actions are stored. This gives finance teams real-time cost intelligence with automated escalation and audit-ready records. Setup Steps Import workflow JSON into your n8n instance. Add Anthropic API credentials. Set Schedule Trigger frequency. Update Workflow Configuration node with budget thresholds per department or cost centre. Add Slack credentials and configure the target channel in the Send Slack Alert node. Set Gmail/SMTP credentials for the Send Executive Report Email node. Prerequisites n8n (cloud or self-hosted), Anthropic API key (Claude), Slack workspace with bot token Use Cases Finance teams automating multi-department budget variance detection and escalation Customization Replace Anthropic Claude with OpenAI GPT-4 or NVIDIA NIM in any agent node Benefits Eliminates manual budget reviews through automated AI-driven cost classification

How It Works This workflow automates industrial asset health monitoring and predictive maintenance using Anthropic Claude across coordinated specialist agents. It targets facility managers, maintenance engineers, and operations teams in manufacturing, energy, and infrastructure sectors where reactive maintenance leads to costly unplanned downtime and asset failures. On schedule, the system ingests asset health data and routes it through a Performance Evaluation Agent that coordinates three specialist agents: Maintenance Scheduling, Parts Readiness, and Lifecycle Reporting. An MCP External Data Tool enriches analysis with real-time contextual data. Results are risk-routed—Critical assets trigger immediate Slack alerts, High-risk assets escalate via email reports, and Routine cases are logged for scheduled maintenance. All paths merge into a unified maintenance log, giving operations teams proactive, audit-ready asset intelligence before failures occur. Setup Steps Import workflow JSON into your n8n instance. Add Anthropic API credentials. Set Schedule Trigger frequency aligned to your asset monitoring cycle. Update Workflow Configuration node with asset thresholds. Configure MCP External Data Tool with your external data source endpoint and authentication. Add Slack credentials and set the target channel in the Notify Critical Alert node. Set Gmail/SMTP credentials for the Email Escalation Report node. Prerequisites n8n (cloud or self-hosted), Anthropic API key (Claude), Slack workspace with bot token Use Cases Facility managers automating condition-based maintenance scheduling across multiple assets Customization Replace Anthropic Claude with OpenAI GPT-4 or NVIDIA NIM in any agent node Benefits Shifts maintenance from reactive to predictive, reducing unplanned downtime significantly

How It Works This workflow automates end-to-end medical claims processing using a multi-agent AI orchestration system built on OpenAI GPT-4. It targets healthcare revenue cycle teams, billing departments, and hospital administrators burdened by manual claims adjudication, coding errors, and payer denials. The workflow triggers on a schedule, loads billing data, and routes it through an Orchestrator Agent that coordinates four specialist sub-agents: Coding Validation, Claims Submission, Denial Detection, and Payer Follow-up. Each agent independently validates, submits, or flags claims. Results are parsed, merged, and routed by risk level. Final metrics and a formatted report close the cycle, giving teams real-time visibility into claim status, denial patterns, and revenue recovery. Setup Steps Import workflow JSON into your n8n instance. Add OpenAI API credentials. Configure Schedule Trigger with desired processing frequency. Update Workflow Configuration node with your billing system endpoint or sample data path. Set Gmail/SMTP credentials for the Escalate to Revenue Specialist email node. Connect Google Sheets or database nodes with appropriate credentials and sheet IDs. Test with simulated billing data before enabling live data sources. Prerequisites n8n, OpenAI API key (GPT-4) and Gmail or SMTP account Use Cases Hospital billing departments automating claims submission and denial follow-up Customization Swap OpenAI for NVIDIA NIM or Anthropic models in any agent node and add Slack alerts alongside email escalation Benefits Reduces manual claims review by 80%+ through parallel AI agent processing

⚖️ HR Sovereign: AI-Powered Onboarding Hub A high-fidelity employee onboarding engine: Intake → Role-Based Enrichment → AI Personalization → IT Provisioning. ⚙️ Core Sovereign Logic Enrichment:** Auto-classifies Tech, Sales, and Leadership roles to drive specific logic tracks. Intelligence:* Uses *AI Agent (GPT-4)** to generate personalized welcome messaging based on job DNA. Atomization:* *Merge PDF** node assembles role-specific policies and benefits into a single high-res package. Provisioning:* Dynamically generates *Jira* hardware/access tickets and *Notion** tracking dashboards. Delivery:* Sends branded HTML emails via *Gmail* and announces hires on *Slack**. 📋 Setup & Prerequisites Intake: Connect your HRIS (BambooHR/Workday) to the Webhook URL. Assets: Organize Drive folders into "Technical", "Leadership", and "Standard" templates. Tracking: Connect your Notion Onboarding Database and Jira IT Project. Metrics: Time_to_Provision, Engagement_Score, Document_Integrity_Hash.