by WeblineIndia
Smart Investor Profiling and Reporting Workflow This workflow is a comprehensive automation engine that bridges the gap between raw client data and expert-level financial advice. Upon receiving a new onboarding form from Google Sheets, the system fetches real-time market data via Alpha Vantage, uses Groq-powered AI to synthesize this data with the client's risk profile and generates a structured investment strategy. It then updates your internal CRM, alerts your team via Slack and emails a personalized, professional investment report directly to the client—all in a matter of seconds. Quick Implementation Steps Import: Upload the JSON file into your n8n canvas. Connect**: Authenticate your Google Sheets, Alpha Vantage (HTTP Request), Groq, Slack and Gmail accounts. Prepare CRM: Create a Google Sheet with these headers: **Timestamp, Name, Age, Income, Profile Summary, Risk Category, Strategy, Email. Set Market Symbol: Open the **Fetch Market Data node and replace RELIANCE.BSE with your preferred stock or index symbol. Deploy: Click **Start Workflow to test, then set to 'Active' for real-time processing. What It Does The workflow functions as an intelligent middleman for financial advisory firms. It starts by capturing a lead's personal details (age, income, risk appetite) from an onboarding spreadsheet. To ensure the advice is timely, it immediately queries a market API to get the latest price trends and volatility metrics. This "dual-context" (client data + market data) is then fed into a high-performance Groq AI model. The AI generates a sophisticated output that includes a written investor profile, a risk classification and a specific portfolio allocation (Equity, Debt, Gold). To maintain data integrity, a structured parser ensures the AI's response is converted into clean data fields that update your master CRM automatically. Finally, the workflow handles the entire communication loop. It sends a highly formatted HTML email to the client containing their custom strategy, while simultaneously posting a detailed summary to your Slack channel. This ensures your team is instantly aware of new high-value leads and their specific needs. Who It's For Financial Advisors & Wealth Managers** who want to provide "instant-gratification" value to new leads. Investment Firms** looking to standardize their risk profiling using AI and real-time data. FinTech Startups** automating the transition from sign-up to personalized dashboard content. Brokerage Operations** teams needing to sync client onboarding with internal CRM databases and team alerts. Requirements to use this workflow n8n accouny: (Self-hosted or Cloud). Alpha Vantage API Key**: For fetching real-time market trends. Groq API Key**: To power the "Analyze Investor Profile" AI node. Google Account**: For Sheets (Onboarding/CRM) and Gmail (Client communication). Slack Workspace**: For internal team notifications. How It Works & Setup Guide 1. Triggering & Rate Control The workflow starts with a Manual Trigger or a Google Sheets fetch. The Rate Limit Control node is set to process one item at a time, ensuring you don't hit API limits on your Market or AI accounts during a bulk upload. 2. Market Intelligence The Fetch Market Data node uses an HTTP Request to pull live data. You must enter your Alpha Vantage API key in the apikey query parameter. The Process Market Data and Generate Market Insights nodes use JavaScript to determine if the current market is "Bullish" or "Bearish" and calculate volatility. 3. AI Analysis & Parsing The Analyze Investor Profile node sends all client and market variables to Groq. The Profile Data Parser ensures the AI's response follows a strict JSON schema, which is vital for the data to be saved correctly in your spreadsheet. 4. Storage & Communication The Update CRM node appends the AI's findings to your spreadsheet. Once the database is updated, the workflow branches to send a Slack Notification to your team and a Send Welcome Email to the client. How To Customize Nodes Change Market Focus: In the **Fetch Market Data node, change the symbol parameter to any ticker your firm specializes in (e.g., SPY for S&P 500). Modify AI Strategy: Edit the prompt in the **Analyze Investor Profile node to include your specific house views or investment products. Slack Channel: Re-select your preferred channel in the **Slack Notification node to ensure alerts go to the right team. Use Case Examples Automated Lead Magnets**: Offering a "Free AI Portfolio Review" on your website that delivers results instantly via email. Client Re-Profiling**: Periodically running the workflow for existing clients to see how their strategy should shift based on new market conditions. Scalable Onboarding**: Managing hundreds of new sign-ups during market volatility without increasing headcount. Advisor Assistance**: Using the AI-generated summary as a "cheat sheet" for advisors before they hop on a first call with a lead. Real-time Risk Alerts**: Notifying the team via Slack specifically when a "High Risk" investor joins during "High Volatility" market periods. Troubleshooting Guide | Issue | Possible Cause | Solution | | :--- | :--- | :--- | | Market Data fails | Invalid or missing API Key | Ensure your Alpha Vantage key is entered in the Fetch Market Data query parameters. | | AI output is "unstructured" | AI failed to follow JSON format | Ensure the Profile Data Parser has "Auto Fix" enabled to catch minor formatting errors. | | CRM not updating | Header mismatch | Verify that your Google Sheet has the exact headers: Timestamp, Name, Age, Income, Profile Summary, Risk Category, Strategy, Email. | | Emails not sending | Gmail OAuth issues | Re-authenticate your Gmail account in the n8n credentials settings. | Need Help? Building market-aware AI agents requires a high level of technical precision. If you need assistance configuring your API keys, customizing the investment logic or integrating this workflow with your existing tech stack, our n8n workflow development team at WeblineIndia is ready to help. Contact WeblineIndia to help you build, customize or scale your professional business automations today!
by Avkash Kakdiya
How it works This workflow automatically handles new GitHub issues by reading and understanding them using AI, then taking multiple actions simultaneously — creating tasks, sending notifications, and keeping everything organized — without any manual effort. Step-by-step Trigger & filter** GitHub Trigger – Listens for issue events on a GitHub repository. If – Filters to only process issues with the action opened. AI analysis** Message a model – Sends the issue title, body, and metadata to GPT-4.1-mini with a structured prompt to extract category, subcategory, severity, priority, estimated effort, assigned team, summary, and suggested action. Parse AI Response – Converts the raw AI text output into a clean structured JSON object and appends a formatted date. Parallel output actions** Append row in sheet – Logs all issue data and AI analysis into a Google Sheets tracker. Create a task – Creates a prioritized ClickUp task with full issue and AI analysis details. Send a message – Posts a formatted Slack alert to #github-alerts with issue details and AI analysis. Send a message1 – Sends an HTML-formatted email via Gmail with the full issue report and AI insights. Create a comment on an issue – Posts the AI analysis summary as a comment directly on the GitHub issue. HTTP Request – Applies AI-generated labels (category + severity) to the GitHub issue via the GitHub API. Why use this? Eliminates manual issue triage by using AI to instantly classify and prioritize every new issue Keeps the entire team informed across Slack, Email, and GitHub simultaneously Auto-creates ClickUp tasks so nothing falls through the cracks Logs every issue to Google Sheets for reporting and trend analysis Responds directly on GitHub so issue reporters get instant acknowledgment
by WeblineIndia
Quick Overview This scheduled workflow pulls the latest AI-related articles from NewsAPI, uses Google Gemini to filter and draft a LinkedIn post plus image prompt, generates an image with OpenAI, uploads the image and post to LinkedIn via its API, and sends status notifications through Gmail and Slack. How it works Runs on a schedule and requests the latest AI/tech articles from NewsAPI. Checks whether the NewsAPI response contains any results and emails via Gmail if no articles are available. Sends the first returned article to Google Gemini to decide whether it is suitable for a professional LinkedIn post. Parses Gemini’s JSON decision, continues only if the article is approved, and emails via Gmail if it is rejected as not relevant. Uses Google Gemini to generate LinkedIn post copy and an AI image prompt from the approved article, then cleans and parses the model output into structured JSON. Generates an image from the prompt with OpenAI, fetches the LinkedIn user profile, registers a LinkedIn media upload, converts the image data for upload, and uploads it to LinkedIn. Creates a LinkedIn UGC post that includes the generated text and uploaded image asset, then sends a success notification to a Slack channel. Setup Add credentials for Google Gemini (PaLM API), OpenAI, LinkedIn OAuth2, Gmail OAuth2, and Slack, and ensure the LinkedIn connection has permission to upload media and publish posts. Replace YOUR_NEWS_API_KEY_HERE in the NewsAPI request URL with your NewsAPI key and adjust the query, language, and pageSize if needed. Update the Gmail recipient addresses used for “no news” and “not relevant” notifications. Set the Slack channel for success notifications. In the LinkedIn image upload step, replace YOUR_GENRATED_UPLOAD_URL with the upload URL returned by LinkedIn’s registerUpload response (and map it dynamically) so the PUT upload targets the correct URL.
by Avkash Kakdiya
Quick Overview This workflow receives new-hire records via a webhook, validates and normalizes the data, then provisions a Google Workspace account, creates an onboarding task in Google Tasks, notifies the IT team in Slack, emails the hiring manager via Gmail, and logs the run to Google Sheets. How it works Receives a new hire payload from the HR system through an HTTP webhook endpoint. Parses the payload, standardizes key fields (name, department, manager, start date), and skips records missing required identity data. If the record is invalid, posts a Slack message to the IT channel for manual review and stops processing. For valid hires, generates a company email address, username, temporary password, ticket title, and a target ready-by date. Creates the new user in the Google Admin Directory (Google Workspace) and opens an IT onboarding checklist as a Google Tasks task. Posts a provisioning summary to the IT Slack channel, emails the manager a day-one checklist via Gmail, and appends the provisioning details to a Google Sheets log. Setup Configure your HR system (or source app) to send a POST request to the workflow webhook path /hr/new-hire using the production webhook URL. Add Google credentials with access to the Admin SDK Directory API (for user creation), Google Tasks, Gmail, and Google Sheets, and ensure the account has the required admin permissions. Add Slack credentials, set the target channel ID for IT onboarding notifications, and update the channel selection if needed. Update the email domain in the generated address (currently @yourcompany.com) and confirm how departments map to Google Workspace org unit paths. Set your Google Sheets document ID and ensure it contains (or can create) a sheet named "Provisioning Log" with matching columns for the appended fields.
by Avkash Kakdiya
How it works This workflow automatically handles every failed Stripe payment by collecting all details, assessing severity, and taking immediate action — alerting your team, creating recovery tasks, escalating repeat failures, notifying the customer, and logging everything — without any manual effort. Step-by-step Trigger & parse** Stripe Trigger – Listens for charge.failed, invoice.payment_failed, and payment_intent.payment_failed events from Stripe. Parse & Enrich Payment Data – Extracts and normalizes customer name, email, plan, amount, failure code, attempt count, and assigns severity (LOW / MEDIUM / HIGH / CRITICAL) and ClickUp priority based on payment amount. High value check** High Value Check – Checks if the failed payment is $1,000 or more to route it to a critical or standard alert path. Slack — Critical Alert – Posts a urgent CRITICAL PAYMENT FAILURE message to Slack with full payment and customer details. Slack — Standard Alert – Posts a standard payment failed message to Slack for lower-value failures. Task creation** ClickUp — Create Recovery Task – Creates a prioritized recovery task in ClickUp with due date set to 24h (critical) or 48h (standard) from failure time. Repeat failure check** Retry Count Check – Checks if the payment has failed more than twice to determine if escalation is needed. Slack — Escalation Alert – Sends a ESCALATION message tagging the billing manager when repeated failures are detected. Customer notification & logging** Send a message – Sends an HTML-formatted Gmail to the customer explaining the failure, reason, and next retry date. Log to Google Sheets – Appends all payment failure details including severity, attempt count, failure code, and Stripe links to a tracking sheet. Create or update a contact – Creates or updates the customer record in HubSpot with their name, email, and plan details. Why use this? Instantly catches every Stripe payment failure across all event types with zero manual monitoring Severity-based routing ensures critical high-value failures get immediate escalated attention Auto-creates ClickUp recovery tasks so every failure has an owner and a deadline Escalates repeated failures automatically so chronic issues never slip through unnoticed Keeps customers informed with professional failure emails while logging everything for reporting and CRM hygiene
by Rajeet Nair
Overview This workflow automates end-to-end contract analysis using AI. It extracts clauses, evaluates risks, tracks obligations, and generates an executive summary from uploaded contracts. By combining multiple AI agents with structured parsing and scoring logic, the workflow provides deep legal insights and automatically flags high-risk contracts. It helps legal, compliance, and business teams review contracts faster, reduce risk, and ensure better decision-making. How It Works Webhook Trigger Accepts contract uploads via API endpoint. Workflow Configuration Defines: Risk threshold Alert recipients Slack notification channel PDF Text Extraction Extracts raw text from uploaded contract files. Parallel AI Analysis Multiple AI agents process the contract simultaneously: Clause Extraction Agent Identifies and categorizes contract clauses Risk Assessment Agent Evaluates clause-level risks Assigns risk levels and recommendations Obligation Extraction Agent Extracts responsibilities, deadlines, and commitments Executive Summary Agent Generates structured contract summary and insights Structured Output Parsing Ensures consistent JSON outputs for all agents Data Aggregation Merges outputs from all AI agents into one dataset Risk Scoring Engine Calculates weighted risk score based on: Clause types Risk levels Missing critical clauses AI confidence scores Generates: Score breakdown Red flags Recommendations Data Preparation Formats contract data for storage and tracking Database Storage Stores: Contract metadata Clauses Obligations Risk scoring results Risk Evaluation Compares calculated risk score against threshold Alert System If risk is high: Sends email alerts Sends Slack notifications Setup Instructions Webhook Setup Configure endpoint (contract-upload) Connect to your document upload system AI Model Setup Add Anthropic (Claude 3.5) or OpenAI credentials Connect model to all AI agent nodes Database Setup Configure Postgres credentials Create tables: contracts contract_clauses contract_obligations risk_scoring Email Integration Configure Gmail or SMTP credentials Set alert recipient emails Slack Integration Add Slack credentials Set channel ID for alerts Configuration Settings Adjust: Risk threshold (e.g., 0.7) Alert recipients Slack channel Use Cases Legal contract review automation Vendor agreement risk assessment Compliance and audit workflows Procurement contract validation AI-powered legal tech solutions Requirements AI model API (Anthropic or OpenAI) Postgres database Email service (Gmail/SMTP) Slack workspace n8n instance Key Features Multi-agent AI contract analysis Clause extraction and categorization Risk scoring with weighted logic Obligation tracking and deadline extraction Executive summary generation Automated high-risk alert system Structured database storage for audit Summary A powerful AI-driven contract intelligence workflow that transforms unstructured legal documents into structured insights, risk scores, and actionable recommendations. It enables faster contract reviews, better risk management, and scalable legal automation.
by Lukasz
Score DNS Threats with VirusTotal, Abuse.ch, HashiCorp Vault and Gemini An end-to-end Cyber Threat Intelligence pipeline that turns raw DNS traffic into actionable security verdicts — without manual triage, without leaking credentials, and without alert fatigue. What this workflow does This workflow ingests passive DNS observations, enriches each indicator with multi-source threat intelligence, and uses Google Gemini as a senior security analyst to produce a single, defensible verdict per indicator. Every step that touches a secret pulls it from HashiCorp Vault at runtime, and only confirmed threats reach your inbox. Problems it solves Manual threat triage takes hours. A SOC analyst checking a suspicious indicator across VirusTotal, ThreatFox, and URLhaus, then writing up the verdict, typically spends 10-20 minutes per IoC. This workflow performs the same correlation in seconds and produces a structured report ready for review or downstream automation. Hardcoded credentials are a breach waiting to happen. API keys, database passwords, and provider tokens commonly end up in workflow JSON, environment files, or git history. This workflow fetches every secret directly from HashiCorp Vault during execution, so credentials never live inside n8n configuration. Alert fatigue trains analysts to ignore real threats. The AI agent applies a detection-first scoring model on a 1–5 scale, with email alerts firing only on confirmed malicious indicators (score ≥ 4). Clean traffic and low-signal noise are silently logged for trend analysis, not pushed to the operator. Single-source intelligence is misleading. Indicators flagged by one provider but absent from others are often false positives — and indicators marked clean by one source may already be active C2 infrastructure tracked by another. This workflow correlates across three independent CTI sources before assigning a verdict. Trusted infrastructure produces noise. Cloud providers, CDNs, and developer platforms (AWS, Cloudflare, GitHub, Bitbucket) frequently appear in threat feeds because attackers abuse them — the platforms themselves are not malicious. The scoring model recognizes "big player" infrastructure and caps the score unless a specific malware family is confirmed, eliminating a major source of false positives. How it works The workflow runs as five coordinated stages: 1. Indicator capture. Passive DNS logs are read from MySQL using credentials retrieved from Vault. Indicators that have not yet been analyzed are queued for enrichment. 2. Multi-source enrichment. Three independent CTI branches run in parallel: VirusTotal** — primary source for IP/domain reputation and ownership ThreatFox (Abuse.ch)** — primary source for active C2 infrastructure and malware family attribution URLhaus (Abuse.ch)** — supporting context on URLs historically hosted at the indicator 3. AI-driven verdict. Google Gemini receives the consolidated intelligence, applies a detection-first scoring policy loaded dynamically from the database, and returns a structured JSON verdict including a numeric score, malicious flag, threat label, English technical summary, and Polish operator commentary. 4. Persistence. Results are written to MySQL with full referential integrity, ready for Grafana dashboards or further automation. 5. Conditional alerting. Only indicators with score ≥ 4 trigger an email notification. Email styling adapts to severity: green for informational, amber for review, red for confirmed threats. Architecture components | Layer | Component | Role | |---|---|---| | Traffic source | Passive DNS (MySQL) | Identifies new IoCs from observed network traffic | | Secret engine | HashiCorp Vault | Provides all credentials and API tokens at runtime | | Intelligence | VirusTotal, ThreatFox, URLhaus | Independent CTI sources for cross-validation | | AI reasoning | Google Gemini | Acts as a senior security analyst, correlating data and generating verdicts | | Persistence | MySQL (partitioned) | Stores results with 6-month automated retention | | Alerting | Gmail via SMTP | Severity-aware notifications, only for confirmed threats | Release v1.0.2-rc1 Highlights This is the release candidate for the first stable v1.0.2 build, available on the Cyber Sentinel GitHub repository. Detection-first scoring (1–5 scale)** — replaces the previous 1–10 scale with a clearer mapping to operator actions: Allow, Monitor, Review, Block, Block + Alert. Dynamic threat scale** — score definitions are loaded from the database at every AI invocation, enabling future self-healing workflows that can auto-tune the scoring model. Source weighting** — VirusTotal and ThreatFox drive the score; URLhaus contributes only as a supporting modifier, eliminating false positives on legitimate platforms. Severity-aware email alerts** — color and header adapt to score (green INFO / amber REVIEW / red ALERT) instead of every indicator triggering a red alarm banner. Partitioned database with automated retention** — DNS queries, network events, and threat indicators are partitioned monthly with automatic cleanup after 6 months. Unified Vault provisioning** — single Ansible playbook handles initialization, unsealing, and secret provisioning idempotently. Full Infrastructure-as-Code deployment** — the entire stack (Nginx, Vault, MySQL, MongoDB, n8n) deploys via Ansible with credentials managed through Ansible Vault. Tested on Proxmox (Debian) and Raspberry Pi 5** — production-grade stability validated on both home-lab and resource-constrained environments. Documentation GitHub repository:** https://github.com/lukaszFD/cyber-sentinel Project documentation:** https://lukaszfd.github.io/cyber-sentinel/ Release notes:** https://github.com/lukaszFD/cyber-sentinel/releases
by Diego Alejandro Parrás
AI job application tracker and interview prep assistant Categories: AI, Productivity, Career Transform your job search from chaos to clarity. This workflow automatically tracks every application, researches companies, and prepares you for interviews with AI-generated materials — all saved to a visual Notion pipeline. Benefits Never lose track of applications** — Every job gets logged automatically with status tracking Walk into interviews prepared** — AI generates likely questions, talking points, and company insights Save 2-3 hours per application** — Research and prep that took hours now happens in seconds Automated follow-up reminders** — Get notified when it's time to send that follow-up email How It Works Submit application via form (paste job URL) or forward your confirmation email AI extracts job details — company, role, requirements, salary, location Interview prep generates — likely questions, suggested talking points, questions to ask Everything saves to Notion — visual pipeline with follow-up dates Daily reminders — Slack notification for applications needing follow-up Required Setup Notion Database Structure Create a Notion database with these properties: | Property Name | Type | Purpose | |---------------|------|---------| | Company | Title | Company name | | Role | Text | Job title | | Status | Select | Applied, Interviewing, Offer, Rejected, Ghosted | | Applied Date | Date | When you applied | | Salary Range | Text | Compensation info | | Job URL | URL | Link to posting | | Location | Text | City/Remote | | Interview Prep | Text | AI-generated prep materials | | Follow Up Date | Date | When to follow up | | Requirements | Text | Key job requirements | | Notes | Text | Your personal notes | Credentials Needed OpenAI API** — For job extraction and interview prep generation Notion** — Connected to your job tracker database Gmail** (optional) — For email forwarding and confirmations Slack** (optional) — For follow-up reminders Use Cases Active job seekers** — Track 20+ applications without spreadsheet chaos Career changers** — Get AI help understanding new industry requirements Recent graduates** — Build interview confidence with generated prep materials Passive searchers** — Keep a running list with minimal effort Set Up Steps Import the workflow into your n8n instance Create Notion database with the structure above (or duplicate template) Connect OpenAI credentials — API key with GPT-5 access recommended Connect Notion credentials — Select your job tracker database Configure Gmail trigger (optional) — Set filter for forwarded job emails Set up Slack webhook (optional) — Choose channel for reminders Test with a sample job posting — Paste a LinkedIn or company careers page URL Customization Tips Edit the interview prep prompt to mention your background/experience Adjust the follow-up reminder interval (default: 7 days) Add additional research sources (LinkedIn, Crunchbase) for richer company data Connect to calendar to block interview prep time automatically Technical Notes Uses Jina AI Reader for web scraping (free tier available) GPT-5-mini recommended for cost efficiency Notion text fields limited to 2000 characters (full prep saved) Daily check runs at 9 AM (configurable) Difficulty Level: Intermediate Estimated Setup Time: 30-45 minutes Monthly Operating Cost: ~$2-5 (based on 50 applications/month with GPT-5-mini)
by WeblineIndia
RFP Automation Workflow with Google Drive, Groq AI (LLaMA 3), Salesforce & Slack > Automate Your Entire RFP Lifecycle This workflow automatically detects new RFP documents uploaded to Google Drive, extracts key requirements using Groq AI (LLaMA 3), generates a complete proposal draft, stores it in Google Docs, creates a Salesforce opportunity, assigns a review task and notifies your team via Slack. Quick Implementation Steps Connect your Google Drive, Groq API, Salesforce, Google Docs and Slack accounts in n8n Set your RFP Intake Folder ID in the trigger node Set a separate Knowledge Base folder for past proposals Customize your company profile in the AI proposal generator node Activate the workflow Upload an RFP → Everything runs automatically What It Does This workflow fully automates the end-to-end process of handling RFP (Request for Proposal) documents. When a new file is uploaded to a specific Google Drive folder, the workflow immediately triggers and downloads the document. It then extracts text from the file and prepares structured metadata such as file name, URL and timestamp. Using Groq-powered AI (LLaMA 3), the workflow analyzes the document and converts unstructured RFP content into structured JSON data. This includes critical fields like client name, project scope, deadlines, budget, technical requirements and evaluation criteria. The workflow ensures that even imperfect AI outputs are safely parsed and normalized. Next, the workflow enhances proposal quality by referencing past proposals stored in a separate knowledge base folder. It then generates a complete, professional proposal draft tailored to the extracted requirements. Finally, it stores outputs in Google Docs and Salesforce, assigns a review task and notifies the team via Slack—ensuring no RFP is missed and every response is standardized. Who It's For IT services companies handling frequent RFP submissions Consulting firms responding to client proposals Digital agencies managing multiple bids Sales and business development teams Enterprises looking to standardize proposal creation Requirements To use this workflow, you need: n8n account (self-hosted or cloud) Google Drive account** RFP Intake Folder Knowledge Base Folder (past proposals) Groq API Key** Salesforce account** Opportunity + Task access Google Docs account** Slack workspace** How It Works & Set Up Step 1: Google Drive Trigger Setup Configure Watch RFP Upload Folder node Replace with your RFP Intake Folder ID Trigger: fileCreated Step 2: File Processing The workflow: Downloads the uploaded file Extracts text (PDF supported) Prepares metadata (file name, ID, URL, timestamp) Step 3: AI Requirement Extraction Configure Groq API credentials Model used: llama-3.3-70b-versatile Extracts structured data such as: Client name Project scope Budget & deadlines Technical requirements Step 4: Clean & Parse Output Code node ensures: JSON is valid Markdown artifacts are removed Fallback structure is used if parsing fails Step 5: Knowledge Base Integration Configure Fetch Past Proposals (KB) node Replace with your separate Knowledge Base folder ID Fetches previous proposal documents for reference Step 6: AI Proposal Generation Uses extracted requirements + KB context Generates a full proposal with sections: Executive Summary Solution Timeline Pricing Team Customize company details inside prompt Step 7: Output Storage Saves proposal to Google Docs Creates Salesforce Opportunity with: Name Deadline Value Description Step 8: Task Creation & Notification Creates a Salesforce Task for review Sends notification to Slack channel How To Customize Nodes AI Nodes Change model (e.g., Mixtral, LLaMA variants) Adjust temperature: 0.1 → structured extraction 0.7 → creative writing Proposal Generator Update: Company name Services Strengths Modify proposal sections if needed Salesforce Node Map custom fields (e.g., Industry__c) Adjust stage names or pipeline stages Slack Node Change channel (#general) Customize notification message Add-Ons You can extend this workflow with: Email notifications (Gmail/Outlook) Approval workflows before submission PDF export of proposal CRM enrichment (HubSpot, Zoho) AI scoring for win probability Auto-deadline reminders Use Case Examples Automatically respond to government RFPs IT companies generating proposals for software projects Agencies handling multiple client bids daily Consulting firms standardizing proposal quality Sales teams tracking opportunities with zero manual entry There can be many more use cases depending on your business workflow and automation needs. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|--------------|---------| | Workflow not triggering | Incorrect folder ID | Verify Google Drive folder ID | | No text extracted | Unsupported file format | Use PDF format | | AI output parsing fails | Unexpected AI response | Check logs in "Clean & Parse AI Output" node | | Salesforce error | Missing custom fields | Remove or create fields in Salesforce | | Proposal not saved | Google Docs credentials issue | Reconnect Google Docs API | | Slack message not sent | Wrong channel or credentials | Verify Slack integration | Need Help? If you need help setting up, customizing or scaling this workflow, our n8n workflow development team at WeblineIndia is happy to help. We can help you: Customize AI prompts for your business Integrate additional CRMs or tools Build advanced automation pipelines Optimize proposal quality using AI Reach out to WeblineIndia for expert support and tailored automation solutions.
by Nguyen Thieu Toan
Smart email forwarder Zoho mail to Gmail with attachment, analysis by AI agent This n8n template automatically monitors a Zoho Mail inbox, analyzes every incoming email with Google Gemini AI, then forwards it — including all attachments — to a designated recipient via Gmail. The admin receives an instant smart digest on Telegram with priority level, deadline, and required action extracted by AI. If you manage a shared inbox and waste time triaging emails before forwarding them, this workflow does it for you — automatically. How it works Zoho Mail Trigger:** Detects new incoming emails matching a configured list of sender addresses. AI Analysis:* *Google Gemini reads the email subject and summary, then classifies the email type, assigns a priority level (High / Medium / Low), extracts deadlines, key numbers, and required actions — all returned as structured JSON. HTML Builder:* Assembles the forwarded email body with an *AI analysis panel** embedded at the top, shared across both sending paths. Attachment handling:* If the email has attachments, the workflow fetches each file via the *Zoho Mail API, renames binary keys to prevent collision, aggregates all files, then attaches them to the outgoing Gmail. Gmail:** Forwards the formatted email — with or without attachments — to the configured recipient. Telegram:** Sends the admin a structured, emoji-rich notification with all AI-extracted data, so decisions can be made without opening the email. How to use Connect credentials: Link your Zoho Mail, Gmail, Google Gemini (googlePalmApi), and Telegram Bot credentials in n8n. Configure in one place: Open the Set Context node and fill in all 6 fields — recipient email, subject prefix, Telegram chat ID, footer text, AI role description, and email category options. Set sender filter: Edit the Zoho Mail Trigger node and update the from field with the sender addresses you want to monitor. Activate the workflow — it runs automatically on every new matching email. Requirements n8n Version:* Built and tested on *n8n 2.9.4+*. *(It is recommended to use the latest n8n version for best compatibility.) Zoho Mail** account with OAuth2 credentials and API access enabled. Gmail** account connected via OAuth2. Google Gemini** API key (via googlePalmApi credential). Telegram Bot** token and a target chat ID. Customizing this workflow Different inbox:* Swap the *Zoho Mail Trigger for a Gmail Trigger, IMAP, or any other email node — the rest of the workflow remains unchanged. Different AI behavior:* Change ai_context and ai_type_options in *Set Context** to adapt the AI to any domain (legal, finance, HR, support) — no prompt editing needed elsewhere. Different notification channel:* Replace the *Telegram node with Slack, Discord, or Microsoft Teams. Persistent storage:* Add a *Google Sheets* or *Airtable** node after Telegram to log every forwarded email with its AI analysis for audit purposes. About the Author Created by: Nguyễn Thiệu Toàn (Jay Nguyen) Email: me@nguyenthieutoan.com Website: nguyenthieutoan.com Company: GenStaff (genstaff.net) Socials (Facebook / X / LinkedIn): @nguyenthieutoan More templates: n8n.io/creators/nguyenthieutoan
by Rishabh Dugar
This workflow automates the full lifecycle of a vehicle insurance claim — from an incoming Gmail email to a signed, watermarked PDF decision letter delivered back to the claimant. It monitors Gmail for emails with a PDF claim attachment, deduplicates them against a Google Sheet, parses the claim PDF using PDF API Hub's OCR, evaluates the claim with OpenAI GPT-4o-mini (or a built-in mock evaluator for testing), and routes it to one of three paths: Approved** — generates a signed approval letter, stamps it APPROVED, uploads to Google Drive, and emails the claimant Rejected** — generates a rejection letter, stamps it REJECTED, uploads to Google Drive, and emails the claimant Manual Review** — notifies your claims adjuster by email for human review Every claim is logged and tracked in a Google Sheet. What you need Gmail OAuth2** — to monitor the claims inbox and send decision emails Google Sheets OAuth2** — for claim logging and duplicate detection Google Drive OAuth2** — to store processed claim PDFs OpenAI API key** — only required when ai_mode is set to openai PDF API Hub API key** — get one free at pdfapihub.com (30 free API calls) Setup Google Sheets Create a Google Sheet with a tab named Claims and add these headers in row 1: claim_id | claimant_name | claimant_email | claim_type | decision | approved_amount | reasoning | risk_flags | summary | processed_at | email_message_id A sample sheet you can clone is linked in the Sticky Note — Overview node inside the workflow. After creating the sheet, open every Google Sheets node in the workflow and point it to your sheet and tab. Google Drive Create two folders in your Drive: Approved Claims and Rejected Claims. Set each folder in the corresponding Upload node. Workflow Config node Edit the Workflow Config Set node to configure: | Field | Description | |---|---| | self_email | Your email — receives duplicate-claim alerts | | claims_adjuster_email | Email for manual review notifications | | ai_mode | mock for testing, openai for production | | mock_decision | APPROVED, REJECTED, or MANUAL_REVIEW (mock mode only) | | company_name | Used in generated PDF letters | | support_phone | Used in generated PDF letters | | appeals_email | Used in generated PDF letters | How it works New Claim Uploaded — Gmail trigger polls every minute for emails with subject containing "insurance claim" and a PDF attachment Check Duplicate in Sheet — looks up the Gmail message ID in the tracking sheet to skip already-processed emails Extract Claim Data — sends the PDF to PDF API Hub's Parse PDF node and returns the full claim text as JSON AI Mode Router — routes to OpenAI or Mock evaluator based on ai_mode config AI Claim Evaluator / Mock AI Evaluator — produces a structured decision with decision, reasoning, approved_amount, risk_flags, and claimant details Parse AI Decision — extracts the structured JSON from the AI response Log Claim to Sheet — appends the claim record to Google Sheets Route by Decision — branches into Approved, Rejected, or Manual Review paths PDF generation — HTML-to-PDF nodes generate branded decision letters using PDF API Hub Sign + Watermark — approval letters are digitally signed and stamped APPROVED or REJECTED Upload + Email — PDFs are uploaded to Drive and the decision is emailed to the claimant Customisation tips Edit the AI system prompt in the AI Claim Evaluator node to match your own underwriting rules and risk thresholds Switch mock_decision in Workflow Config between APPROVED, REJECTED, and MANUAL_REVIEW to test all three paths without making any API calls Edit the HTML inside the letter-generation nodes to match your company branding Adjust the >$50,000 → MANUAL_REVIEW threshold directly in the AI prompt PDF API Hub nodes used Parse PDF** — extracts claim text from the uploaded PDF attachment HTML to PDF** ×2 — generates the approval and rejection letter PDFs Sign PDF** — adds an authorised digital signature to the approval letter Add Watermark** ×2 — stamps APPROVED (green) or REJECTED (red) across the letter A note on quality This is not an AI-generated template. AI was used as a coding assistant for certain pieces, but every path and every input has been manually validated end-to-end — including sending a real claim email, receiving the decision email back, verifying the signed and watermarked PDF was uploaded to the correct Google Drive folder, and confirming the Google Sheet was updated with the claim record. Both the OpenAI path and mock mode were tested manually across all three decision outcomes: APPROVED, REJECTED, and MANUAL_REVIEW.
by Cheng Siong Chin
How It Works This workflow automates regulatory compliance monitoring and policy violation detection for enterprises managing complex governance requirements. Designed for compliance officers, legal teams, and risk management departments, it addresses the challenge of continuous policy adherence across organizational activities while reducing manual audit overhead.The system initiates on schedule, triggering compliance checks across operational data. Solar compliance data generation simulates policy document collection from various business units. Claude AI performs comprehensive policy validation against regulatory frameworks, while parallel NVIDIA governance models analyze specific compliance dimensions through structured outputs. The workflow routes findings by compliance status: violations trigger immediate escalation emails to compliance teams with detailed Slack notifications, warnings generate supervisor alerts with tracking mechanisms, and compliant activities proceed to standard documentation. All execution paths merge for consolidated audit trail creation, logging enforcement actions and generating governance reports for regulatory submissions. Setup Steps Configure Schedule Compliance Check node with monitoring frequency Add Claude AI credentials in Workflow Configuration and Policy Validation nodes Set up NVIDIA API keys for governance output parser and agent modules in respective nodes Connect Gmail authentication for compliance team alerts and configure recipient distribution lists Integrate Slack workspace credentials and specify compliance channel webhooks Prerequisites Claude API access, NVIDIA API credentials, Gmail/Google Workspace account Use Cases Financial services regulatory compliance (SOX, GDPR), healthcare HIPAA monitoring Customization Add industry-specific regulatory frameworks, integrate document management systems Benefits Reduces compliance audit time by 70%, ensures consistent policy application across departments