Workflow Templates

3 nodes719 views

Connect Airtable Contacts to telli for Automated AI Voice Call Scheduling

by Paulo Ramirez

Upload your CRM contacts to telli and schedule AI voice-agent calls Introduction to telli and AI Voice-Agent Calls telli is an innovative platform that provides AI-powered voice agents capable of making calls and performing tasks tailored to specific customer use cases. These AI voice-agents can handle a wide range of communication tasks, from appointment scheduling to customer support, with remarkable efficiency and natural conversation flow. This template is designed for businesses and organizations looking to automate their outbound calling processes using telli's AI voice-agents in conjunction with Airtable as their CRM. It solves the problem of manual call scheduling and data transfer between your CRM and calling system, saving time and reducing human error. Prerequisites telli account Airtable base with contact information n8n instance Step-by-Step Setup Guide n8n Setup: Create a new workflow in n8n. Add the Airtable node to connect to your CRM table. telli API Configuration: Log in to your telli dashboard. Locate and copy your API key under telli - Settings - API/Webhooks. Workflow Configuration: Add two HTTP Request nodes to your n8n workflow. Set the "Authorization" header in both POST requests, replacing the value with your telli API key. Configure the first request to use the /add-contact endpoint. Set up the second request to use the /schedule-call endpoint. Data Mapping: Map the relevant fields from your Airtable node to the telli API requests. Testing and Activation: Run a test execution of your workflow. Once satisfied with the results, activate the workflow. API Endpoint Details Add Contact Endpoint URL**: https://api.telli.com/v1/add-contact Method**: POST Headers**: Authorization: YOUR-API-KEY Content-Type: application/json Payload**: { "external_contact_id": "string", "salutation": "string", "first_name": "string", "last_name": "string", "phone_number": "string", "email": "jsmith@example.com", "contact_details": {}, "timezone": "string" } Schedule Call Endpoint URL**: https://api.telli.com/v1/schedule-call Method**: POST Headers**: Authorization: YOUR-API-KEY Content-Type: application/json Payload**: { "contact_id": TELLI-CONTACT-ID, "agent_id": "string", "max_retry_days": 123, "call_details": { "message": "Hello, this is your friendly reminder!", "questions": [ { "fieldName": "email", "neededInformation": "email of the customer", "exampleQuestion": "What is your email address?", "responseFormat": "email string" } ] }, "override_from_number": "string" } Use Cases This template is versatile and can be applied to various scenarios, including: Lead Qualification*: Automatically schedule calls to new leads entered in your CRM. Appointment Reminders*: Set up calls to remind clients of upcoming appointments. Customer Feedback*: Schedule follow-up calls after product deliveries or service completions. Uploading Multiple Contacts For bulk operations, you have two options: Loop Node: Include a Loop node in your n8n workflow to process multiple contacts sequentially. Batch Endpoints: Instead of /add-contact and /schedule-call, use telli's batch endpoints: /add-contacts-batch: Add multiple contacts within an array. /schedule-calls-batch: Schedule multiple calls at once. Example of batch endpoint usage: { "contacts": [ {"name": "John Doe", "phone": "+1234567890"}, {"name": "Jane Smith", "phone": "+1987654321"} ] } By leveraging this template, you can seamlessly integrate your Airtable CRM with telli's powerful AI voice-agents, automating your outbound calling process and enhancing your customer communication strategy.

Dynamically switch between LLMs for AI Agents using LangChain Code

by Mario

Dynamically switch between LLMs for AI Agents using LangChain Code Purpose This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them. What it does This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model. How it works A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node. The AI output is later validated by a Sentiment Analysis Node If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM The loop ends either when the result passed the requirements or when all LLMs have been used before. Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI Account, alternatively you can swap the LLM nodes with ones from a different provider like Anthropic after the import. How to use Beware that the order of the used LLMs is determined by the order they have been added to the workflow, not by the position on the canvas. After cloning this workflow into your environment, open the chat and send this example message: > I really love waiting two weeks just to get a keyboard that doesn’t even work. Great job. Any chance I could actually use the thing I paid for sometime this month? Most likely you will see that the first validation fails, causing it to loop back to the generation node and try again with the next available LLM. Since AI responses are unpredictable, the results and number of tries will differ for each run. Disclaimer Please note, that this workflow can only run on self-hosted n8n instances, since it requires the LangChain Code Node.

🤖AI & Machine Learning

4 nodes3807 views

Automated Resume Screening & Ranking with Llama 4 AI and Google Workspace

by Sean Lon

Target Audience You will find this workflow or template perfect if you are in the internal talent acquisition teams, recruitment agencies, HR professionals, and hiring managers seeking to bulk automate the initial screening of CVs and resumes. Eg. Automatically get result of candidate who has been shortlisted/rejected with its rationale and score automatically. By eliminating manual evaluation and screening, you get smart AI-Agent helping you to have standardized efficient, and scalable solution for handling large volumes of applications. With bulk automation, you can focus strategic decision-making rather than tedious screening tasks, ensuring a faster, more accurate, and fair hiring process. Key focus This workflow focusses on having a more organized file-folder management, trackable candidate cv, maintainable job description, autonomous ai-agent. Organized Folder-File Structure – CVs are automatically categorized based on their status, ensuring a structured workflow and easy retrieval Candidate Tracker – A real-time tracking system records the state of each CV, allowing recruiters to monitor the shortlisted, rejected, or KIV (Keep in View) candidates. AI Agent for Decision Automation – The AI autonomously orchestrates screening decisions, replacing manual LLM configurations with dynamic AI-driven evaluations for scalability and accuracy. Maintainable Job Description Management – A structured job description file ensures continuous updates, keeping hiring criteria flexible and aligned with recruitment needs. Email Notifications – The system automatically sends receipt confirmations upon processing completion, providing timely updates to recruiters. Features - Workflow Automated Resume Screening Workflow This workflow leverages Groq Llama4 for intelligent resume analysis, speeding the screening process by generating a matching score, result (shortlisted/rejected/kiv), and key insights/rationale into their suitability for provided job description. Step-by-Step Process: Monitors Google Drive:** Listens and checks for new resume cv in google drive . Retrieve Resume:** Downloads the CV resumes from google drive . Extract Resume Data:* Extract *text content** from CV resume PDF files Extract Job Description Data:* Extract *text content** from job description Analyze with Groq:** Generate a matching score based on job requirements. [SCORE: 1-10] Provide decision into their job suitability. [SHORTLISTED/REJECTED/KIV] Provide actionable insights into their job suitability. [REASON] This ensures a fast, efficient, and accurate screening process, eliminating manual evaluation. Setup Guide Step-by-Step Instructions Ensure all credentials are ready and setup (groq, gdrive ,gmail, gsheet, gdoc) View official n8n documentation on node setup accordingly. See also the notes of setup . Folder & File Setup 1. Create a google-drive folder like this View directory example 2. Create a job description like this View file example 3. Configure a tracker like this ( Candidate Name, AI Score,AI Verdict, AI Reason) View file example email conversations report as you like. You are ready to go!

4 nodes2463 views

Automate Etsy Data Mining with Bright Data Scrape & Google Gemini

by Ranjan Dailata

Who this is for? The Automate Etsy Data Mining with Bright Data Scrape & Google Gemini workflow is designed for eCommerce analysts, product researchers, and AI developers seeking to extract actionable insights from Etsy listings at scale. It is ideal for: eCommerce Entrepreneurs** - Researching product demand and competition. Market Analysts** - Tracking pricing, reviews, and trends across Etsy categories. Product Managers** - Identifying niche opportunities and design inspirations. Data Scientists & AI Engineers** - Automating product intelligence pipelines. Growth Hackers** - Leveraging Etsy insights to refine product-market fit. What problem is this workflow solving? Manually browsing Etsy to analyze product listings, pricing, reviews, and seller activity is slow, inconsistent, and unscalable. Scraping Etsy requires unlocking JavaScript-heavy content and structuring noisy data for analysis. This workflow solves: Automated and scalable scraping of Etsy product listings using Bright Data’s infrastructure. A fully paginated data structured Estry production data extraction via the Google Gemini LLM. Enables faster decision-making for product research and competitive analysis via the fully automated paginated data extraction. What this workflow does Receives input: Sets the Esty URL for the data extraction and analysis. Uses Bright Data's Web Unlocker to extract content from relevant sites. Cleans and preprocesses the scraped content for readability. Sends the content to Google Gemini for: Enriched results including: Data persistence over the disk. Sends the response to a target system via Webhook notification. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). Update the Set Esty Search Query for setting the brand content URL and the Bright Data Zone name. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Input Sources** : Replace the static URL with dynamic input from Google Sheets, Webhook, or Airtable to research multiple niches. Prompt Customization** : Adjust Gemini prompts to extract specific insights for example: List key features of the product Summarization of the review themes Data Output Options** : Update the Webhook notification to save data to: Google Sheets Notion or Airtable SQL/NoSQL Slack/Email

4 nodes2136 views

Cybersecurity Assistant with GPT-4, Telegram Bot & Command Execution

by mariskarthick

QuantumDefender AI is a next-generation intelligent cybersecurity assistant designed to harness the symbolic strength of quantum computing’s promise alongside cutting-edge AI capabilities. This sophisticated agent empowers SOC analysts, red teamers, and security researchers with rapid threat investigation, operational automation, and intelligent command execution—all driven by GPT-4 and integrated tools, accessible through Telegram or on any medium. 🔑 Key Features: Expert-Level Cybersecurity Research & Analysis: Leverages powerful AI models to deliver clean, detailed, domain-specific insights across detection, remediation, and offensive security. Command & Control: Executes Linux shell commands, autonomous scripts, and system operations securely in isolated environments. Real-Time Web Intelligence: Utilizes integrated Langsearch API to provide timely internet research with contextual relevance. Calendar & Scheduling Automation: Manage Google Calendar events or any similar application(create, update, delete, retrieve) dynamically from chat. Multi-Tool Orchestration: Combines calculator functions, internet searches, command execution, and messaging for comprehensive operational support. Telegram-native Chatbot: Delivers an adaptive, memory-informed, and interactive conversational experience with immediate typing indicators and high responsiveness. Conversation & Session Management: Maintains context-aware, session-based memory to enable smooth, multi-turn dialogues with individual users. Sends “typing…” indicators during processing to ensure an interactive, user-friendly chat experience. Operates exclusively within Telegram, delivering rich, timely responses and leveraging all Telegram bot capabilities. Execution Intelligence & Safety: Fully autonomous in deciding which tools to invoke, how frequently, and in what sequence to fulfill user requests comprehensively and responsibly. Operates within a secure temporary folder environment to contain all command executions safely and avoid persistent or harmful side effects. Enforces strict safety protocols to avoid running malicious or destructive commands, maintaining ethical standards and compliance. Use Cases: Cybersecurity researchers and operators seeking an intelligent assistant to accelerate investigations and automate routine tasks. Red team professionals requiring on-the-fly command execution and information gathering integrated with tactical chat interactions. SOC teams aiming to augment their alert triage and incident handling workflows with AI-powered analysis and action. Anyone looking for a robust multi-tool AI chatbot integrated with real-world operational capabilities. Setup Requirements: OpenAI API key for GPT-4.1-nano language processing. Telegram Bot API credentials with proper webhook setup to receive and respond to messages. Google OAuth credentials for Calendar integration if calendar features are used. SSH access credentials for executing commands on remote hosts, if remote execution is enabled. Internet connectivity for the Langsearch web search API. Customization & Extensibility: The workflow is built modularly with n8n’s flexible node system. Users can extend it by adding more tools, integrating other services (ticketing, threat intel, scanning tools), or modifying interaction logic to suit specialized operational needs and environments. Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast

Auto-Post Dev.to Articles to LinkedIn with Airtable Tracking & Telegram Alerts

by Krishna Kumar Eswaran

🧠 Problem This Solves: For developers and creators, consistently posting quality content on LinkedIn can be time-consuming. This workflow automates the process by: Fetching the latest Dev.to articles Posting them to LinkedIn twice daily Preventing duplicates using Airtable Sending success alerts to Telegram This ensures you're always active on LinkedIn, with zero manual effort. 👥 Who This Template Is For Developers who want to build their presence on LinkedIn Tech creators or solo founders looking to grow an audience Community/page managers who want regular, curated content Busy professionals aiming for consistent LinkedIn engagement without doing it manually ⚙️ Workflow Breakdown This automation runs twice a day (9:00 AM and 7:00 PM) and performs the following steps: Fetches Dev.to articles based on a tag Checks Airtable to avoid reposting the same article Posts to LinkedIn if it’s new Sends a Telegram message after posting successfully 🧩 Step-by-Step Setup Instructions ✅ 1. Airtable Configuration Create a new base in Airtable with just one table and one column: Table Name: PostedArticles Column: ArticleID (Single line text – stores the unique ID of each Dev.to article posted) This column is used to track posted articles and prevent duplicates. 🔗 2. Dev.to API Setup Use the following endpoint in the HTTP Request node: arduino Copy Edit https://dev.to/api/articles?tag=YOUR_TAG_HERE&per_page=10 Replace YOUR_TAG_HERE with a tag like android, webdev, ai, etc. 💬 3. Telegram Bot Setup Open @BotFather in Telegram and create a new bot Save the bot token Get your chat ID using @userinfobot or via Telegram API Add a Telegram node in n8n using this token and chat ID This will notify you when a post is successfully published. 🧾 4. LinkedIn Setup Create a LinkedIn Developer App Use OAuth2 to connect it in n8n Choose to post on either a user profile or a company page 🧱 5. n8n Workflow Structure Here’s the basic structure of the workflow: Cron Node – Triggers at 9:00 AM and 7:00 PM daily HTTP Request – Fetches latest articles from Dev.to Airtable Search – Checks if ArticleID already exists IF Node – Filters new vs. already-posted articles LinkedIn Post – Publishes new article Airtable Create – Saves the new ArticleID Telegram Message – Sends success confirmation 🛠️ Customization Tips Change the Dev.to tag in the API URL Modify LinkedIn post format (add hashtags, emojis, personal notes) Adjust posting times in the Cron node Use additional filters (e.g., only post articles with a cover image or certain word count)

5 nodes525 views

Create Customized Google Slides Presentations from CSV Data for Cold Outreach 🚀

by JPres

👥 Who Is This For? Sales and marketing teams seeking efficient, hands‑free generation of personalized slide decks for each prospect from CSV lead lists. 🛠 What Problem Does This Solve? Manually editing presentation decks for large lead lists is slow and error‑prone. This workflow fully automates: Importing and parsing CSV lead data Logging leads and outputs in Google Sheets Duplicating a master Slides template per lead Injecting lead‑specific variables into slides 🔄 Node‑by‑Node Breakdown | Step | Node | Purpose | | ---- | ---------------------------------------- | -------------------------------------------------------- | | 1 | New Leads Arrived | Detect new CSV uploads in Drive | | 2 | File Type? | Filter for .csv files only | | 3 | Download by ID | Download the CSV content | | 4 | Create new Sheet | Create a Google Sheet to record lead data | | 5 | Combine Empty New Document with CSV Data | Structure each lead record for slide creation | | 6 | Merge Data for new Lead Document | Map template placeholders to lead values | | 7 | Get all Leads | Retrieve sheet rows to iterate through each lead | | 8 | MoveToLeadListFolder | Move processed CSV to an archive folder | | 9 | Copy Slides Template | Make a copy of the master Slides deck | | 10 | Create Custom Presentation | Replace placeholders in the copied deck with lead data | | 11 | Add Presentation ID to Lead | Write the generated presentation URL back into the Sheet | ⚙️ Pre‑conditions / Requirements n8n with Google Drive, Sheets, and Slides credentials A master Google Slides deck with placeholder tokens (e.g. {{Name}}, {{Company}}) A Drive folder for incoming CSV lead files ⚙️ Setup Instructions Import this workflow into your n8n instance. Configure the New Leads Arrived node to watch your CSV folder. Enter your Google credentials in the Drive, Sheets, and Slides nodes. Specify the master Slides template ID in the Copy Slides Template node. In Create Custom Presentation, map slide tokens to sheet column names. Disable “Keep Binary Data” in Copy Slides Template to conserve memory. Upload a sample CSV (with headers like Name, Company, Metric) to test. 🎨 How to Customize Add or remove variables by editing the CSV headers and updating the mapping in Merge Data for new Lead Document. Insert an AI/natural‑language node before slide creation to generate more advanced and personalized text blocks. Use SplitInBatches to throttle API calls and avoid rate‑limit errors. Add error‑handling branches to capture and log failed operations. 🔐 Security and Privacy The workflow uses placeholder variables for file and folder IDs, so no actual IDs are exposed in the template. Ensure OAuth scopes are limited to only the required Google APIs.

Automate YouTube Uploads with AI-Generated Metadata from Google Drive

by JPres

👥 Who Is This For? Content creators, marketing teams, and channel managers who want a simple, hands‑off solution to upload videos and automatically generate optimized metadata from video transcripts. 🛠 What Problem Does This Solve? Manual video uploads with proper metadata creation is time‑consuming and repetitive. This workflow fully automates: Monitoring a specific Google Drive folder for new video uploads Seamless YouTube upload processing Transcript extraction for context understanding AI‑powered generation of titles, descriptions, and tags Metadata application to uploaded videos without manual intervention 🔄 Node‑by‑Node Breakdown | Step | Node Purpose | |------|---------------------------------------------------------------------| | 1 | New Video? (Trigger) – Monitors specified Google Drive folder | | 2 | Download New Video – Retrieves the video file from Google Drive | | 3 | Upload to YouTube – Uploads the video to YouTube with initial settings | | 4 | Get Transcript – Extracts transcript from the uploaded video | | 5 | Adjust Transcript Format – Formats raw transcript for processing | | 6 | Create Description – Generates SEO‑optimized description | | 7 | YT Tags (Message Model) – Creates relevant tags based on content | | 8 | YT Title (Message Model) – Generates compelling title | | 9 | Define File Path Upload Format (Optional) – Structures data paths | | 10 | Update Video’s Metadata – Applies generated title, description, tags| ⚙️ Pre‑conditions / Requirements n8n with Google Drive and YouTube API credentials configured (stored as n8n credentials/variables; no hard‑coded IDs) Dedicated Google Drive folder for video uploads YouTube channel with proper upload permissions AI service access for transcript processing and metadata generation Sufficient storage for temporary video handling ⚙️ Setup Instructions Import this workflow into your n8n instance. Configure Google Drive credentials; reference folder ID via n8n variable (do not hard‑code). Set up YouTube API credentials with upload and edit permissions. Specify the target Google Drive folder ID in the New Video? trigger node (via variable). Configure AI service credentials for transcript and metadata generation. Adjust message templates for title, description, and tag creation. Test with a small video file before production use. 🎨 How to Customize Modify AI prompts to match your channel’s tone and style. Add conditional logic based on video categories or naming conventions. Implement notification systems to alert when uploads complete. Create custom metadata templates for different content types. Include timestamps or chapter markers based on transcript analysis. Add social media sharing nodes to announce new uploads. ⚠️ Important Notes Video quality is preserved through the upload process. Consider YouTube API quotas when handling multiple uploads. Transcript quality affects metadata generation results. Videos are initially uploaded without visibility adjustments. Processing time depends on video length and transcript complexity. 🔐 Security and Privacy Store API credentials and folder IDs as n8n Credentials/Variables—remove any hard‑coded tokens or IDs. Video files are processed temporarily and not stored permanently. Limit Google Drive folder access to authorized users only. Manage YouTube upload permissions carefully (use OAuth/service accounts). Ensure compliance with organizational data‑handling policies.

Auto-Post Medium.com Articles to LinkedIn with Telegram Alerts

by Krishna Kumar Eswaran

🧠 Problem This Solves Manually sharing Medium articles to LinkedIn daily can be repetitive and time-consuming. This automation: Fetches the latest Medium articles based on a tag (e.g., android) Posts them on LinkedIn twice daily Uses Airtable to prevent duplicates Sends a confirmation to Telegram once posted Stay consistently active on LinkedIn without lifting a finger. 👥 Who This Template Is For Developers who write or follow Medium content Tech creators or founders looking to grow an audience Community or page managers needing regular curated posts Busy professionals who want hands-free LinkedIn engagement ⚙️ Workflow Breakdown This automation runs at 9:00 AM and 7:00 PM daily and performs these steps: Fetch articles from MediumAPI.com by tag Check Airtable to prevent reposting the same article Post on LinkedIn if it’s new Store the article ID in Airtable Send a Telegram message after successful posting 🧾 Step-by-Step Setup Instructions ✅ 1. Airtable Configuration Create a base with: Table Name: PostedArticles Column: ArticleID (Single line text – to track posted articles) 🔗 2. MediumAPI Setup Go to https://mediumapi.com Sign up and generate your API key from the dashboard Use this API endpoint in an HTTP node: GET https://mediumapi.com/api/tag/YOUR_TAG/latest Headers: Authorization: Bearer YOUR_API_KEY Replace YOUR_TAG with a topic like android, ai, webdev, etc. 💬 3. Telegram Bot Setup Go to @BotFather and create a new bot Save the bot token Use @userinfobot to get your Telegram chat ID Add a Telegram node in n8n with the token + chat ID 🔗 4. LinkedIn Setup Create a LinkedIn Developer App Connect it via OAuth2 in n8n Choose to post on your profile or company page 🧱 5. n8n Workflow Structure Node Type Description Cron Triggers the flow twice a day HTTP Request Fetches articles from MediumAPI.com Airtable Search Checks if article ID already exists IF Node Skips duplicates LinkedIn Post Publishes to your LinkedIn profile/page Airtable Create Stores posted article ID Telegram Node Sends success notification 🛠️ Customization Tips Change the tag in the API URL to match your niche Add hashtags or personal comments to the LinkedIn message Schedule different posting times in the Cron node Filter Medium posts based on length or title keywords (optional)

5 nodes796 views

Cold Outreach Automation: Scrape Local Leads with Dumpling AI & Call via Vapi

by Yang

Who is this for? This template is for sales teams, agencies, or local service providers who want to quickly generate cold outreach lists and automatically call local businesses with a Vapi AI assistant. It’s perfect for automating cold calls from scraped local listings with no manual dialing or research. What problem is this workflow solving? Finding leads and initiating outreach calls can be time-consuming. This workflow automates the process: it scrapes business listings from Google Maps using Dumpling AI, extracts phone numbers, filters out incomplete data, formats the numbers, and uses Vapi to make outbound AI-powered calls. Every call is logged in Google Sheets for follow-up and tracking. What this workflow does Starts manually and pulls search queries (e.g., "plumbers in Austin") from Google Sheets. Sends each query to Dumpling AI’s Google Maps scraping endpoint. Splits the returned business data into individual leads. Extracts key info like business name, website, and phone number. Filters to only keep leads with valid phone numbers. Formats phone numbers for Vapi dialing (adds +1). Calls each business using Vapi AI. Logs each successful call in a Google Sheet. Setup Google Sheets Setup Create a sheet with business search queries in the first column (e.g., best+restaurants+in+Chicago) Make sure the tab name is set and authorized in your credentials. Connect your Google Sheets account in the Get Search Keywords from Google Sheets node. Dumpling AI Setup Go to dumplingai.com Generate an API Key and connect it as a header token in the Scrape Google Map Businesses using Dumpling AI node Vapi Setup Sign into Vapi and create an assistant Get your assistantId and phoneNumberId Insert these into the JSON payload of the Initiate Vapi AI Call to Business node Add your Vapi API key to the credentials section Call Logging Create another tab in your sheet (e.g., “leads”) with these headers: company name phone number website This will be used in the Log Called Business Info to Sheet node How to customize this workflow to your needs Modify the business search terms in your Google Sheet to target specific industries or locations. Add filters to exclude certain businesses based on ratings, keywords, or location. Update your Vapi assistant script to match the type of outreach or pitch you’re using. Add additional integrations (e.g., CRM logging, Slack notifications, follow-up emails). Change the trigger to run on a schedule or webhook instead of manually. Nodes and Functions Breakdown Start Workflow Manually: Initiates the automation manually for testing or controlled runs. Get Search Keywords from Google Sheets: Reads search phrases from the spreadsheet. Scrape Google Map Businesses using Dumpling AI: Sends each search query to Dumpling AI and receives matching local business data. Split Each Business Result: Breaks the returned array of businesses into individual records for processing. Extract Business Name, Phone and website: Extracts title, phone, and website from each business record. Filter Valid Phone Numbers Only: Ensures only entries with a phone number move forward. Format Phone Number for Calling: Adds a +1 country code and strips non-numeric characters. Initiate Vapi AI Call to Business: Uses the business name and number to initiate a Vapi AI outbound call. Log Called Business Info to Sheet: Appends business details into a Google Sheet for tracking. Notes You must have valid API keys and authorized connections for Dumpling AI, Google Sheets, and Vapi. Make sure to handle API rate limits if you're running the workflow on large datasets. This workflow is optimized for US-based leads (+1 country code); adjust the formatting node if calling internationally.

Tesla 1hour Indicators Tool (Mid-Term Technical Analysis AI)

by Don Jayamaha Jr