Automated Customer Service Ticket Creation & Notifications with Asana & WhatsApp

This workflow offers an effective way to handle a chatbot's functionality, making use of multiple tools for information retrieval, conversation context storage, and message sending. It's a setup tailored for a Slack environment, aiming to offer an interactive, AI-driven chatbot experience. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot. After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message. In case user sends a message to the bot, the text and user data is stored in Google Sheets. To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node. Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉 Setup Instructions To get this workflow up and running, you'll need to: 👇 Create a WhatsApp template message on the Meta Business portal. Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages. Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal. Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates. Connect your Google Sheets account for data storage and management. Check out the documentation page. ⚠️ Important Notes WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent. The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.

A robust n8n workflow designed to enhance Telegram bot functionality for user management and broadcasting. It facilitates automatic support ticket creation, efficient user data storage in Redis, and a sophisticated system for message forwarding and broadcasting. How It Works Telegram Bot Setup: Initiate the workflow with a Telegram bot configured for handling different chat types (private, supergroup, channel). User Data Management: Formats and updates user data, storing it in a Redis database for efficient retrieval and management. Support Ticket Creation: Automatically generates chat tickets for user messages and saves the corresponding topic IDs in Redis. Message Forwarding: Forwards new messages to the appropriate chat thread, or creates a new thread if none exists. Support Forum Management: Handles messages within a support forum, differentiating between various chat types and user statuses. Broadcasting System: Implements a broadcasting mechanism that sends channel posts to all previous bot users, with a system to filter out blocked users. Blocked User Management: Identifies and manages blocked users, preventing them from receiving broadcasted messages. Versatile Channel Handling: Ensures that messages from verified channels are properly managed and broadcasted to relevant users. Set Up Steps Estimated Time**: Around 30 minutes. Requirements**: A Telegram bot, a Redis database, and Telegram group/channel IDs are necessary. Configuration**: Input the Telegram bot token and relevant group/channel IDs. Configure message handling and user data processing according to your needs. Detailed Instructions**: Sticky notes within the workflow provide extensive setup information and guidance. Live Demo Workflow Bot: Telegram Bot Link (Click here) Support Group: Telegram Group Link (Click here) Broadcasting Channel: Telegram Channel Link (Click here) Keywords: n8n workflow, Telegram bot, chat ticket system, Redis database, message broadcasting, user data management, support forum automation

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

With this workflow you get a fully automated AI powered Support-Agent for your WooCommerce webshop. It allows customers to request information about things like: the status of their order the ordered products shipping and billing address current DHL shipping status How it works The workflow receives chat messages from an in a website integrated chat. For security and data-privacy reasons, does the website transmit the email address of the user encrypted with the requests. That ensures that user can just request the information about their own orders. An AI agent with a custom tool supplies the needed information. The tool calls a sub-workflow (in this case, in the same workflow for convenience) to retrieve the required information. This includes the full information of past orders plus the shipping information from DHL. If otherr shipping providers are used it should be simple to adjust the workflow to query information from other APIs like UPS, Fedex or others.

This n8n template is designed to assist and improve customer support team member capacity by automating the resolution of long-lived and forgotten JIRA issues. How it works Schedule Trigger runs daily to check for long-lived unresolved issues and imports them into the workflow. Each Issue is handled as a separate subworkflow by using an execute workflow node. This allows parallel processing. A report is generated from the issue using its comment history allowing the issue to be classified by AI - determining the state and progress of the issue. If determined to be resolved, sentiment analysis is performed to track customer satisfaction. If negative, a slack message is sent to escalate, otherwise the issue is closed automatically. If no response has been initiated, an AI agent will attempt to search and resolve the issue itself using similar resolved issues or from the notion database. If a solution is found, it is posted to the issue and closed. If the issue is blocked and waiting for responses, then a reminder message is added. How to use This template searches for JIRA issues which are older than 7 days which are not in the "Done" status. Ensure there are some issues that meet this criteria otherwise adjust the search query to suit. Works best if you frequently have long-lived issues that need resolving. Ensure the notion tool is configured as to not read documents you didn't intend it to ie. private and/or internal documentation. Requirements JIRA for issues management OpenAI for LLM Slack for notifications Customising this workflow Why not try classifying issues as they are created? One use-case may be for quality control such as ensuring reporting criteria is adhered to, summarising and rephrasing issue for easier reading or adjusting priority.