Jira Software and OpenAI integration

Complete incident workflow from detection through resolution to post-mortem, with full organizational context from Port's catalog. This template handles both incident triggered and resolved events from PagerDuty, automatically creating Jira tickets with context, notifying teams via Slack, calculating MTTR, and using Port AI Agents to schedule post-mortem meetings and create documentation. How it works The n8n workflow orchestrates the following steps: On Incident Triggered: PagerDuty webhook — Receives incident events from PagerDuty via POST request. Event routing — Routes to triggered or resolved flow based on event type. Port context enrichment — Uses Port's n8n node to query your software catalog for service context, on-call engineers, recent deployments, runbooks, and past incidents. AI severity assessment — OpenAI assesses severity based on Port context and recommends investigation actions. Escalation routing — Critical incidents automatically escalate to leadership Slack channel. Jira ticket creation — Creates incident ticket with full context, investigation checklist, and recommended actions. Team notification — Notifies the team's Slack channel with incident details and resources. On Incident Resolved: Port context extraction — Gets post-incident context from Port including stakeholders and documentation spaces. MTTR calculation — Calculates mean time to resolution from incident timestamps. Post-mortem generation — AI generates a structured post-mortem template with timeline. Port AI Agent scheduling — Triggers Port AI Agent to schedule post-mortem meeting, invite stakeholders, and create documentation. Resolution notification — Notifies team with MTTR, post-mortem document link, and meeting details. Metrics logging — Logs MTTR metrics back to Port for service reliability tracking. Setup [ ] Register for free on Port.io [ ] Configure Port with services, on-call schedules, and deployment history [ ] Set up Port AI agents for post-mortem scheduling [ ] Connect PagerDuty webhook for incident events [ ] Configure Jira project for incident tickets (use project key 'INC' or customize) [ ] Set up Slack channels for alerts (#incidents and #leadership-alerts) [ ] Add OpenAI credentials for severity assessment [ ] Test with a sample incident event [ ] You should be good to go! Prerequisites You have a Port account and have completed the onboarding process. Port's integrations are configured (GitHub, Jira, PagerDuty if available). You have a working n8n instance (Cloud or self-hosted) with Port's n8n custom node installed. PagerDuty account with webhook capabilities. Jira Cloud account with appropriate project permissions. Slack workspace with bot permissions to post messages. OpenAI API key for severity assessment and post-mortem generation. ⚠️ This template is intended for Self-Hosted instances only.

Webhook from Payment Provider → Jira Finance Ticket → Slack Invoice Follow-up Automation This workflow automates failed subscription renewal processing by validating webhook data, using AI to analyze urgency and churn risk, creating a Jira Finance Task and notifying the finance team via Slack. If required fields are missing, it sends an error alert for manual review instead. ⚡ Quick Implementation Steps (Start Using in 60 Seconds) Import workflow JSON into n8n. Add Jira & Slack credentials. Configure webhook URL /payment-failed-renewal in payment provider. Test with: { "customerId": "C-101", "customerEmail": "[email protected]", "subscriptionId": "S-500", "amount": 39.99 } Activate workflow. What It Does This automation connects your payment system with your financial operations. When a subscription renewal fails, the payment provider sends a webhook. The workflow validates the fields, uses OpenAI to analyze the payment failure reason (determining urgency & churn risk), routes high-value failures to high priority, creates a Jira task with an AI-drafted recovery email and alerts the finance team on Slack. If required data is missing, the workflow prevents incomplete Jira tickets by routing the event to an error handler and sending a detailed Slack alert listing all missing fields and full payload for manual inspection. Who’s It For Finance & billing departments SaaS companies with recurring billing Teams using Jira for billing operations Slack-based financial support teams Companies wanting automated revenue recovery workflows Requirements to Use This Workflow n8n instance OpenAI API Key (or compatible LLM credential) Jira Software account with permissions for FIN project Slack bot token with channel posting rights Payment provider that supports POST JSON webhooks Webhook configured to: https://YOUR-N8N-URL/webhook/payment-failed-renewal How It Works & How To Set Up Step-by-Step Flow Webhook receives payment failure payload. Validation node checks required fields: customerId customerEmail subscriptionId amount AI Analysis: OpenAI analyzes failure reason, sets urgency, and drafts email. Logic: Switch node routes High Value (>$500) to 'High' priority. Jira Finance Task created (with AI draft). Slack message sent (with Churn Risk score). Setup Steps Step 1 — Webhook Setup Method: POST Path: payment-failed-renewal Step 2 — Jira Setup Select Jira credentials in Create Jira Finance Ticket node. Ensure: Project: FIN Issue type: Task Step 3 — Slack Setup Add Slack credentials to both Slack nodes. Select finance alert channel. Step 4 — OpenAI Setup Add OpenAI credentials in the AI Analysis node. Step 5 — Test { "customerId": "CUST-001", "customerEmail": "[email protected]", "subscriptionId": "SUB-1001", "amount": 19.99 } Step 6 — Activate Enable the workflow. How To Customize Nodes Webhook Add Basic Auth Add token-based security Add JSON schema validation Validate Payload Enhance with: Email format validation Numeric validation for amount Auto-fallback values Jira Node Customize: Ticket summary structure Labels (billing-recovery, urgent, etc.) Add custom fields Change issue type or project Slack Nodes Enhance: Mentions: @finance-team Threads instead of channel posts Rich blocks, buttons, or attachments Add-ons (Optional Enhancements) Automated email to customer for payment recovery Retry count–based escalation (e.g., retry ≥ 3 → escalate to manager) Log data to Airtable / Google Sheets Sync events into CRM (HubSpot, Salesforce, Zoho) Notify Sales for high-value customer failures Use Case Examples Stripe renewal payment fails → Create Jira task → Slack finance alert. Chargebee retry attempts exhausted → Notify billing team immediately. Declined credit card → Jira ticket with failure reason. Razorpay/PayPal renewal failure → Automated follow-up. Webhook missing data → Slack error alert ensures nothing is silently ignored. Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | Webhook not triggering | Wrong URL / method | Use POST + correct endpoint | | Jira ticket missing | No permissions or invalid payload | Check Jira permissions + required fields | | Slack shows undefined values | Missing fields in payload | Confirm payload structure | | Error alert triggered incorrectly | Field names mismatch | Match exact names: customerId, customerEmail, subscriptionId, amount | | Payment provider not sending events | Firewall/CDN blocking | Whitelist the n8n webhook URL | | Workflow silent | Not activated | Turn workflow ON | Need Help? If you want help customizing this workflow or extending it into a complete revenue recovery automation suite: WeblineIndia can support you with: Jira & Slack automation pipelines Payment provider webhook integrations Finance workflow optimization AI-based billing insights End‑to‑end automation solutions Reach out anytime for expert implementation or enhancements.

Automated Social Media Lead Processing with AI Summaries, Slack Alerts & Jira Ticketing This workflow automatically collects new lead messages from social media platforms, LinkedIn or web forms, filters relevant marketing inquiries using keywords, classifies and summarizes the lead with AI, logs it to Google Sheets, creates a Jira task and sends Slack notifications. Additionally, it generates weekly lead reports for team insights. It reduces manual triage, ensures no valid inquiry is missed and keeps your team updated with both immediate notifications and summary reports. Quick Start – Implementation Steps Connect your webhook to your social media inbox, LinkedIn, Twitter or web form. Add your OpenAI, Google Sheets, Jira and Slack credentials. Enable the workflow. Send a test message to confirm Google Sheets logging, Slack notification and Jira task creation. Activate the scheduler for weekly reports to track lead performance. What It Does This workflow performs the following key tasks: Filters incoming messages for marketing-related keywords like ad request, promo request, collaboration, partnership or social media inquiry. Uses OpenAI GPT to classify the lead into categories such as Sales, Support, Partnership, Influencer Inquiry or General Lead. Generates a short AI summary of the message. Logs structured lead data to Google Sheets, including username, source, category, summary and timestamp. Creates a Jira task automatically with summary, description, category and received time. Sends a Slack notification to alert the team instantly. Runs a scheduled workflow that aggregates weekly leads and sends a weekly report to Slack. This ensures a structured, automated pipeline for capturing, summarizing and assigning leads efficiently. Who’s It For Marketing and sales teams managing leads from social media and web forms. Agencies handling client campaigns and inquiries. Businesses that want automated notifications and ticketing. Teams using Slack and Jira for daily operations. Requirements to Use This Workflow n8n account or self-hosted instance. Webhook-enabled social media inbox or lead form. OpenAI API Key. Slack Bot Token with channel posting permission. Jira Software Cloud API credentials. Google Sheets credentials. Predefined keyword list for filtering messages. How It Works & Setup Steps Get DM (Webhook Trigger) Receives new messages from social media or web forms and starts the workflow. Lead Keyword Filter (Code Node) Filters incoming messages for predefined marketing keywords and removes irrelevant or spam messages. AI Lead Classifier (OpenAI Node) Classifies the lead into categories (Sales, Support, Partnership, Influencer Inquiry, General Lead) and generates a one-line summary using GPT-4.1. AI Output Parser (Code Node) Parses AI JSON output and merges it with original message data, adding timestamp and structured fields. Store Lead (Google Sheets Node) Logs structured lead data to Google Sheets including username, source, category, summary and timestamp. Create Task (Jira Node) Automatically creates a Jira story or task in your selected project with the AI summary, category and timestamp. Send a Summary (Slack Node) Sends a formatted message to your selected Slack channel, alerting your team of the new lead. Weekly Reporting Schedule Trigger** – triggers the weekly reporting workflow. Extract Lead Data** – fetches all logged leads from Google Sheets. Weekly Lead Filter** – filters data to include leads from the last week. Report Data Formatter** – calculates total leads, category counts, source counts and example leads. Weekly Report Slack** – sends a formatted weekly lead summary to Slack. How to Customize Nodes Keyword Filter Add or remove keywords in the JavaScript code to match your specific lead types or campaigns. AI Classification Update the OpenAI prompt for different summary lengths, tones, or lead categories. Google Sheets Logging Map additional columns like email, phone or campaign source as needed. Jira Fields Customize summary, description, labels, priority or assignees based on your project requirements. Slack Message Format Modify emojis, line breaks and formatting to suit your team’s Slack notifications. Add-Ons (Extend the Workflow) Send email alerts for high-priority leads. Trigger WhatsApp replies using an API provider. Integrate with CRMs like HubSpot, Zoho or Salesforce. Add sentiment analysis to detect frustrated or VIP users. Automate daily or weekly analytics reports to Slack. Use Case Examples Collecting Instagram, LinkedIn and Twitter DMs and logging them to Google Sheets. Creating automated Jira tickets for marketing inquiries. Sending instant Slack notifications for new leads. Filtering out irrelevant messages and only processing valid marketing leads. Generating weekly lead summary reports for team review. Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | No leads appearing | Webhook not receiving messages | Check webhook URL and ensure messages are sent correctly | | AI summary empty | OpenAI API key invalid or model limit reached | Regenerate API key / check usage | | Jira task not created | Missing required Jira fields or incorrect project ID | Add required fields or update Jira project settings | | Slack message not sent | Wrong channel ID or missing permissions | Reconnect Slack credentials | | Filter passes 0 items | Keywords do not match | Update or expand keyword list in filter node | Need Help? If you need assistance setting up this workflow, customizing nodes, building add-ons or automating more processes, our n8n workflow development team at WeblineIndia is happy to help. We can guide you through integrations, scaling or building end-to-end automation systems tailored to your business.

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

Customer Feedback Automation Workflow with Webhook, OpenAI, Jira & Slack This workflow collects customer feedback from a webhook, validates the incoming data, analyzes the sentiment using OpenAI and creates Jira tasks for negative or feature-request feedback. It also generates an automated weekly summary using OpenAI and delivers it to Slack. It helps teams stay informed, skip manual reviews and act quickly on customer issues. Quick Start – Implementation Steps Set up the Webhook URL in your application to send customer feedback. Configure Slack, Jira and OpenAI credentials in n8n. Adjust sentiment rules or Jira fields if needed. Activate the workflow — you’re ready to collect and process feedback automatically. What It Does This workflow automates the entire lifecycle of customer feedback handling. When someone submits feedback, the system checks if the required information (feedback text and sentiment) is present. If the payload is invalid or incomplete, the team immediately receives a Slack notification to take action. If the feedback is valid, OpenAI analyzes the content and identifies the sentiment as positive, negative, neutral or a feature suggestion. Based on this result, the system automatically creates a Jira issue for negative feedback or feature requests, ensuring nothing important is missed. Alongside real-time processing, the workflow also compiles a weekly summary. Every week, it gathers all Jira issues created through feedback and sends them to OpenAI for summarization. The summary is then posted to Slack so the team gets a clean, easy-to-read review of customer sentiment trends. Who’s It For This workflow is ideal for: Customer support teams Product managers QA and development teams Companies collecting user feedback Businesses wanting automated sentiment analysis and reporting Requirements to Use This Workflow To fully use this workflow, you need: An n8n instance (self-hosted or cloud) A Webhook endpoint to receive customer feedback A Slack workspace with API access A Jira Software Cloud account An OpenAI API key with access to GPT-4.1 or similar Basic understanding of JSON payloads How It Works Collect Feedback – Receives customer data through an n8n webhook. Validate Payload – Checks required fields; bad input triggers Slack alerts. Determine Sentiment – Sends feedback text to OpenAI for sentiment classification. Conditional Routing – Negative or feature-request items move forward; others are ignored. Create Jira Task – Automatically logs an issue for follow-up. Weekly Summary – A scheduled trigger collects all Jira issues created during the week. Generate Report – Sends all issues to OpenAI for a clean weekly summary. Delivered to Slack – The summary is posted for the team to review. Setup Steps Import the workflow JSON file into n8n. Configure your credentials: Slack Jira OpenAI Update the webhook URL in your application. Edit Jira project ID or fields if required. Customize Slack channel IDs. Enable the schedule trigger (weekly summary). Activate the workflow. How To Customize Nodes Customize Sentiment Rules Modify the IF node that checks OpenAI output: Add new sentiment labels (e.g., “bug”, “urgent”) Adjust rules for what should create a Jira issue Customize Jira Task Fields In the Create Jira Task node, you can change: Project ID Issue type Summary and description templates Labels or assignees Customize Slack Messages Update Slack node text blocks to: Format alerts Add more details Send messages to different channels Add-Ons (Optional Enhancements) You can extend this workflow with: Email notifications for high-priority feedback Auto-reply emails to customers acknowledging their feedback Google Sheets logging for historical data Dashboard creation using Airtable or Notion Multi-language sentiment detection Auto-tagging Jira issues with sentiment categories Use Case Examples Customer Complaint Handling – Automatically detect negative feedback and create a Jira issue. Feature Request Collection – Route suggestions to the product backlog. Weekly Sentiment Reporting – Keep managers updated on trends. Slack Alerts for Bad Payloads – Notify the team when someone sends incomplete or incorrect feedback. Automated Feedback Triage – Assign tasks to specific Jira users based on sentiment or keywords. You can extend to many more such similar use cases. Troubleshooting Guide | Issue | Possible Cause | Solution | |-----------------------------|-----------------------------------------------|---------------------------------------------------| | Slack alert not sent | Wrong API credentials or channel ID | Check Slack credentials and update channel ID | | Jira issue not created | Incorrect project ID or issue type | Verify Jira configuration in n8n | | OpenAI sentiment not reading| Wrong JSON path in IF node | Re-check $json.output[0].content[0].text | | Weekly summary not generated| Schedule trigger disabled | Enable the Schedule Trigger node | | Webhook not receiving data | Application not pointing to correct URL | Verify webhook URL in your app | Need Any Help? If you need any help setting up this workflow, troubleshooting or adding custom features, our n8n experts at WeblineIndia is always here to support. We can help you automate more processes, integrate external systems or build complete workflow solutions tailored to your business.

Description This workflow turns scattered user feedback into a structured product backlog pipeline. It collects feedback from three channels (Telegram bot, Google Form/Sheets, and Gmail), normalizes it, and sends it to an AI model that: Classifies the feedback (bug, feature request, question, etc.) Extracts sentiment and pain level Estimates business impact and implementation effort Generates a short summary Then a custom RICE-style priority score is computed, a Jira ticket is created automatically, a Notion page is generated for documentation, and a monthly product report is sent by email to stakeholders. It helps product & support teams move from “random feedback in multiple tools” to a repeatable, data-driven product intake process with zero manual triage. Context In most teams, feedback is: spread across emails, forms, and chat messages manually copy–pasted into Jira (when someone remembers) hard to prioritize objectively nearly impossible to review at the end of the month This workflow solves that by: Centralizing feedback from Telegram, Google Forms/Sheets, and Gmail Automatically normalizing all inputs into the same JSON structure Using AI to categorize, tag, summarize, and score each request Calculating a RICE-based priority adapted to your tiers (free / pro / enterprise) Creating a Jira issue with all the context and acceptance criteria Generating a Notion page for each feedback+ticket pair Sending a monthly “Product Intelligence Report” by email with insights & recommendations The result: less manual work, better prioritization, and a clear story of what users are asking for. Target Users This template is designed for: Product Managers and Product Owners SaaS teams with multiple feedback channels Support / CS teams that need a structured escalation path Project Managers who want objective, data-driven prioritization Any team that wants “feedback → backlog” automation without building a custom platform Technical Requirements You’ll need: Google Sheets credential Gmail credential Telegram Bot + Chat ID Google Form connected to a Google Sheet Jira credential (Jira Cloud) Notion credential OpenAI/ Anthropic credential for the AI analysis node An existing Jira project where tickets will be created A Notion database or parent page where feedback pages will be stored Workflow Steps The workflow is organized into four main sections: 1) Triggers (Multi-channel Intake) Telegram Trigger – Listens for new messages sent to your bot Google Form / Sheet Trigger – Listens for new form responses / rows Gmail Trigger – Listens for new emails matching your filter (e.g. [Feedback] in subject) All three paths send their payloads into a “Data Normalizer” node that outputs a unified structure: 2) Request Treated and Enriched (AI Analysis) Instant Reply (Telegram only) – Sends a quick “Thanks, we’re analysing your feedback” message User Enrichment – Enriches user tier based on mapping Message a Model (AI) classifies the feedback extracts tags scores sentiment, pain, business impact, effort generates a short summary & acceptance criteria JSON Parse / Merge – Merges AI output back into the original feedback object 3) Priority Calculation & Jira Ticket Creation Priority Calculator applies a RICE-style formula using: pain level business impact implementation effort user tier weight assigns internal priority: P0 / P1 / P2 / P3 maps to Jira priority: Highest / High / Medium / Low Create Jira Issue – Creates a ticket with: summary from AI description including raw feedback, AI analysis, and RICE breakdown labels based on tags priority based on the calculator Post-processing – Prepares a clean payload for notifications & logging IF (Source = Telegram) – Sends a rich Telegram message back to the user with: Jira key + URL category, priority, RICE score, tags, and estimated handling time Append to Google Sheet (Analytics Log) – Logs each feedback with: source, user, category, sentiment, RICE score, priority, Jira key, Jira URL Create Notion Page – Creates a documentation page linking: the feedback the Jira ticket AI analysis acceptance criteria 4) Monthly Reporting (Product Intelligence Report) Monthly Trigger – Runs once a month Query Google Sheet – Fetches all feedback logs for the previous month Aggregate Monthly Stats – Computes: feedback volume breakdown by category / sentiment / source / tier / priority average RICE, pain, and impact top P0/P1 issues and top feature requests Message a Model (AI) – Generates a written “Product Intelligence Report” with: executive summary key insights & trends top pain points strategic recommendations Parse Response: Extracts structured insights + short summary Create Notion Report Page with: metrics, charts-ready tables, insights, and recommendations Append Monthly Log to Google Sheet – Stores high-level stats for historical tracking Send Email with a formatted HTML report to stakeholders with: key metrics top issues recommendations link to the full Notion report Key Features Multi-channel intake: Telegram + Google Forms/Sheets + Gmail AI-powered triage: automatic category, sentiment, tags, and summary RICE-style priority scoring with tier weighting Automatic Jira ticket creation with full context Notion documentation for each feedback and for monthly reports Google Sheets analytics log for exploration and dashboards Monthly “Product Intelligence Report” sent automatically by email Designed to be adaptable: you can plug in your own labels, tiers, and scoring rules Expected Output When the workflow is running, you can expect: A Jira issue created automatically for each relevant feedback A confirmation email A Telegram confirmation message when the feedback comes from Telegram A Google Sheet filled with normalized feedback and scoring data A Notion page per feedback/ticket with AI analysis and acceptance criteria Every month: a Notion “Monthly Product Intelligence Report” page a summary email with key metrics and insights for your stakeholders How it works Trigger – Listens to Telegram / Google Forms / Gmail Normalize – Converts all inputs to a unified feedback format Enrich with AI – Category, sentiment, pain, impact, effort, tags, summary Score – Computes RICE-style priority and maps to Jira priority Create Ticket – Opens a Jira issue + Notion page + logs to Google Sheets Notify – Sends Telegram confirmation (if source is Telegram) Report – Once a month, aggregates everything and sends a Product Intelligence Report Tutorial Video 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

Automate Bug Reports: GitHub Issues → AI Analysis → Jira Tickets with Slack & Discord Alerts Automatically convert GitHub issues into analyzed Jira tickets with AI-powered severity detection, developer assignment, and instant team alerts. Overview This workflow captures GitHub issues in real-time, analyzes them with GPT-4o for severity and categorization, creates enriched Jira tickets, assigns the right developers, and notifies your team across Slack and Discord—all automatically. Features AI-Powered Triage**: GPT-4o analyzes bug severity, category, root cause, and generates reproduction steps Smart Assignment**: Automatically assigns developers based on mentioned files and issue context Two-Way Sync**: Posts Jira ticket links back to GitHub issues Multi-Channel Alerts**: Rich notifications in Slack and Discord with action buttons Time Savings**: Eliminates 15-30 minutes of manual triage per bug Customizable Routing**: Easy developer mapping and priority rules What Gets Created Jira Ticket: Original GitHub issue details with reporter info AI severity assessment and categorization Reproduction steps and root cause analysis Estimated completion time Automatic labeling and priority assignment GitHub Comment: Jira ticket link AI analysis summary Assigned developer and estimated time Team Notifications: Severity badges and quick-access buttons Developer assignment and root cause summary Color-coded priority indicators Use Cases Development teams managing 10+ bugs per week Open source projects handling community reports DevOps teams tracking infrastructure issues QA teams coordinating with developers Product teams monitoring user-reported bugs Setup Requirements Required: GitHub repository with admin access Jira Software workspace OpenAI API key (GPT-4o access) Slack workspace OR Discord server Customization Needed: Update developer email mappings in "Parse GPT Response & Map Data" node Replace YOUR_JIRA_PROJECT_KEY with your project key Update Slack channel name (default: dev-alerts) Replace YOUR_DISCORD_WEBHOOK_URL with your webhook Change your-company.atlassian.net to your Jira URL Setup Time: 15-20 minutes Configuration Steps Import workflow JSON into n8n Add credentials: GitHub OAuth2, Jira API, OpenAI API, Slack, Discord Configure GitHub webhook in repository settings Customize developer mappings and project settings Test with sample GitHub issue Activate workflow Expected Results 90% faster bug triage (20 min → 2 min per issue) 100% consistency in bug analysis Zero missed notifications Better developer allocation Improved bug documentation Tags GitHub, Jira, AI, GPT-4, Bug Tracking, DevOps, Automation, Slack, Discord, Issue Management, Development, Project Management, OpenAI, Webhook, Team Collaboration

🎧 IT Voice Support Automation Bot – Telegram Voice Message to JIRA ticket with OpenAI Whisper > Automatically process IT support requests submitted via Telegram voice messages by transcribing, extracting structured data, creating a JIRA ticket, and notifying relevant parties. 🧑‍💼 Who’s it for Internal teams that handle IT support but want to streamline voice-based requests. Employees who prefer using mobile/voice to report incidents or ask for support. Organizations aiming to integrate conversational AI into existing support workflows. ⚙️ How it works / What it does A user sends a voice message to a Telegram bot. The system checks whether it’s an audio message. If valid, the audio is: Downloaded Transcribed via OpenAI Whisper Backed up to Google Drive The transcription and file metadata are merged. The merged content is processed through an AI Agent (GPT) to extract structured request info. A JIRA ticket is created using the extracted data. The IT team is notified via Slack (or other channels). The requester receives a Telegram confirmation message with the JIRA ticket link. If the input is not audio, a polite rejection message is sent. 📌 Key Features Supports voice-based ticket creation Accurate transcription using Whisper Context-aware request parsing using GPT-4.1 mini Fully automated ticket creation in JIRA Notifies both IT and the original requester Cloud backup of original voice messages (Google Drive) 🛠️ Setup Instructions Prerequisites | Component | Required | |----------|----------| | Telegram Bot & API Key | ✅ | | OpenAI Whisper / Transcription Model | ✅ | | Google Drive Credentials (OAuth2) | ✅ | | Google Sheets or other storage (optional) | ⬜ | | JIRA Cloud API Access | ✅ | | Slack Bot or Webhook | ✅ | Workflow Steps Telegram Voice Message Trigger: Starts the flow when a user sends a voice message. Is Audio Message?: If false → reply "only voice is supported" Download Audio: Download .oga file from Telegram. Transcribe Audio: Use OpenAI Whisper to get text transcript. Backup to Google Drive: Upload original voice file with metadata. Merge Results: Combine transcript and metadata. Pre-process Output: Clean formatting before AI extraction. Transcript Processing Agent: GPT-based agent extracts: Requester name, department Request title & description Priority & request type Submit JIRA Request Ticket: Create ticket from AI-extracted data. Setup Slack / Email / Manual Steps: Optional internal routing or approvals. Inform Reporter via Telegram: Sends confirmation message with JIRA ticket link. 🔧 How to Customize Replace JIRA with Zendesk, GitHub Issues, or other ticketing tools. Change Slack to Microsoft Teams or Email. Add Notion/Airtable logging. Enhance agent to extract department from user ID or metadata. 📦 Requirements | Integration | Notes | |-------------|-------| | Telegram Bot | Used for input/output | | Google Drive | Audio backup | | OpenAI GPT + Whisper | Transcript & Extraction | | JIRA | Ticketing platform | | Slack | Team notification | Built with ❤️ using n8n

This automated n8n workflow provides an AI-powered email classifier for support emails, automatically categorizing incoming emails and routing them to the appropriate inbox, creating a Jira task, and sending an autoresponse within minutes. Main Components IMAP Email** - Monitors incoming support emails via IMAP OpenAI** - Classifies emails into categories (billing, bug, feature) Switch** - Routes emails based on the classified category Send Billing Email** - Forwards billing-related emails to the billing inbox Send Bug Email** - Forwards bug-related emails to the bugs inbox Send Feature Email** - Forwards feature-related emails to the features inbox Create Jira Task** - Creates a task in Jira for each classified email Send Autoresponse** - Sends a confirmation email to the sender Essential Prerequisites IMAP access to the support email account OpenAI API key SMTP server credentials for sending emails Jira account with API access Customization Guide Update IMAP host and credentials in the IMAP Email node Adjust OpenAI model or prompt in the OpenAI node Modify email addresses ([email protected], [email protected], [email protected]) in Send Email nodes Configure Jira project ID and credentials in the Create Jira Task node Change autoresponse text in the Send Autoresponse node Features 🤖 AI Classification:** Uses OpenAI to categorize emails into billing, bug, or feature 🔄 Automated Routing:** Routes emails to the correct inbox based on category 🎯 Task Creation:** Creates a Jira task for each support request 📧 Autoresponse:** Sends instant confirmation to the sender ⚡ Optimizations Made:** Streamlined email processing with single-pass classification Parameters to Configure imap_host: Your IMAP server address imap_user: IMAP username (e.g., [email protected]) imap_password: IMAP password openai_api_key: OpenAI API key smtp_host: Your SMTP server address smtp_user: SMTP username smtp_password: SMTP password jira_project_id: Your Jira project ID jira_user: Jira username jira_api_token: Jira API token How to Use Copy the JSON code from the artifact Open your n8n workspace Select “Import from JSON” or “+” → “From JSON” Paste the JSON code Configure parameters in the respective nodes with your credentials and settings Run the workflow Workflow Actions Classify:** Uses OpenAI to categorize incoming emails Route:** Forwards emails to the appropriate inbox Create:** Generates a Jira task for each email Respond:** Sends an autoresponse to the sender