AI client onboarding agent: auto welcome email generator
AI Client Onboarding Agent: Auto Welcome Email Generator
Subtitle: From Form Response to Personalized Welcome Email
π Overview
This workflow automates the client onboarding process. When a new client fills in the Google Form, their data flows into Google Sheets β gets structured β an AI model (Gemini) generates a personalized onboarding email β and finally, Gmail sends it directly to the client.
Think of it as your automated client success assistant.
π’ Section 1: Trigger β Capture New Client
π Node: Google Sheets Trigger
- Watches for new rows added to the βOnboardingβ Google Sheet.
- Starts the workflow whenever a new client submits the onboarding form.
π‘ Why useful? You never have to check the sheet manually β the workflow kicks off the moment a client signs up.
π© Example Input (from form):
- Name: Sarah Ali
- Email: sarah@startup.com
- Company: GreenTech Solutions
- Services Needed: Branding + Website
π¦ Section 2: Structure Client Data
π Nodes:
-
Client Dataβ Formats the raw form submission into a clean text summary (Name, Email, Company, Service, Extra Info). -
Client Checklistβ Prepares a standard onboarding checklist with items like:- Account setup
- Welcome call scheduled
- Document collection
- Service configuration
- Onboarding session
- First milestone review
π‘ Why useful? It makes sure the AI has all key details + a clear structure before writing the email.
π£ Section 3: AI-Generated Email
π Nodes:
Basic LLM Chainβ Prompted to write a professional onboarding email body.Google Gemini Chat Modelβ Supports the LLM chain with Gemini 2.0 Flash for fast generation.
π§ Prompt Logic:
- Starts with:
Hi [Client Name], - Includes personalized fields (Name, Company, Services Needed).
- Inserts onboarding checklist steps.
- Ends with:
Best regards, Your [Company Name] Team
π‘ Why useful? Instead of a generic welcome, each client gets a personalized email that feels human-written.
π© Example Output Email:
> Hi Sarah Ali, > > Welcome to GreenTech Solutions! π > > Hereβs your onboarding plan: > > 1. Account setup > 2. Welcome call scheduled > 3. Document collection > 4. Service configuration > 5. Onboarding session > 6. First milestone review > > Weβre excited to start working with you on Branding + Website. > > Best regards, > Your GreenTech Solutions Team
π‘ Section 4: Send Email
π Node: Gmail
- Sends the AI-generated email to the clientβs email address.
- Subject line:
Welcome to Our Service, [Client Name]
π‘ Why useful? No delays β the client gets a personalized welcome instantly after filling the form.
π΄ Section 5: Error Handling
π Nodes:
Error Handlerβ Listens for any errors during execution.Execution Failureβ Logs failed runs.Execution Completedβ Confirms successful runs.
π‘ Why useful? Ensures nothing gets stuck silently β youβll always know if something fails.
π Workflow Summary
| Section | Node(s) | Purpose | Benefit | | ----------------- | ----------------------------- | ------------------------------------------ | ------------------------------ | | π’ Trigger | Google Sheets Trigger | Detect new client submissions | Fully automated start | | π¦ Structure | Client Data, Client Checklist | Prepare structured client info + checklist | Clean, reliable input for AI | | π£ AI Generation | Basic LLM Chain, Gemini | Generate personalized onboarding email | Professional + tailored emails | | π‘ Send Email | Gmail | Deliver onboarding email | Instant communication | | π΄ Error Handling | Error Handler, NoOp nodes | Handle success/failure states | Reliable + transparent process |
π Benefits
- Zero manual effort β Clients get emails automatically.
- Consistency β Every client follows the same onboarding structure.
- Personalization β Emails include name, company, and services.
- Reliability β Built-in error handling ensures smooth execution.
- Scalability β Works whether you onboard 10 or 1,000 clients.
n8n AI Client Onboarding Agent: Auto Welcome Email Generator
This n8n workflow automates the generation and sending of personalized welcome emails to new clients based on entries in a Google Sheet. It leverages AI to craft engaging messages and handles potential errors gracefully.
What it does
This workflow streamlines the client onboarding process by:
- Monitors Google Sheets: Triggers when a new row is added to a specified Google Sheet, indicating a new client.
- Generates Welcome Email Content: Uses a Langchain Basic LLM Chain with the Google Gemini Chat Model to generate a personalized welcome email.
- Prepares Email Data: Organizes the generated email content and client details into a structured format.
- Sends Welcome Email: Dispatches the personalized welcome email via Gmail to the new client.
- Handles Errors: If any step in the main workflow fails, an error workflow is triggered, which currently performs a "No Operation" (can be extended to send notifications, log errors, etc.).
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Google Sheets Account: A Google account with access to Google Sheets.
- Gmail Account: A Google account with access to Gmail for sending emails.
- Google Gemini API Key: Access to the Google Gemini Chat Model (via Langchain integration).
- n8n Credentials: Configured credentials for Google Sheets, Gmail, and Google Gemini within n8n.
Setup/Usage
- Import the workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up a Google Sheets credential for the "Google Sheets Trigger" node.
- Set up a Gmail credential for the "Gmail" node.
- Set up a Google Gemini Chat Model credential for the "Google Gemini Chat Model" node.
- Configure Google Sheets Trigger:
- Specify the Spreadsheet ID and Sheet Name of the Google Sheet where new client data will be added.
- Ensure the sheet contains relevant columns for client information (e.g.,
Client Name,Client Email,Service Purchased, etc.) which will be used by the AI model.
- Configure Basic LLM Chain:
- Review and adjust the prompt for the "Basic LLM Chain" node to guide the AI in generating the welcome email. Ensure it references the incoming data from Google Sheets appropriately (e.g.,
{{ $json["Client Name"] }}).
- Review and adjust the prompt for the "Basic LLM Chain" node to guide the AI in generating the welcome email. Ensure it references the incoming data from Google Sheets appropriately (e.g.,
- Configure Edit Fields (Set):
- Verify the fields being set for the email (e.g.,
to,subject,text). Adjust as needed to map data from previous nodes.
- Verify the fields being set for the email (e.g.,
- Configure Gmail Node:
- Ensure the "To" field is correctly mapped to the client's email address (e.g.,
{{ $json["Client Email"] }}). - Verify the "Subject" and "Text" fields are pulling the generated content from the "Edit Fields (Set)" node.
- Ensure the "To" field is correctly mapped to the client's email address (e.g.,
- Activate the Workflow: Once configured, activate the workflow. It will now automatically run whenever a new row is added to your specified Google Sheet.
Related Templates
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch π LinkedIn β Wessel Bulte
π How to transform unstructured email data into structured format with AI agent
This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From β senderβs email address To β recipientβs email address Subject β email subject line Summary β short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits β Full Automation: Eliminates manual reading and data entry from incoming emails. β Multi-Source Integration: Handles data from different email providers seamlessly. β AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. β Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. β Time Efficiency: Processes emails in real time, improving productivity and response speed. *β Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
Dynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution
AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) π§ AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative β all powered by AI logic, sir. --- π‘ Key Advantages β‘ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. π§ AI Qualification Engine An OpenAI-powered Agent evaluates the leadβs industry, region, and needs to generate a persona summary and routing rationale. π Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. π¬ Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. π Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- βοΈ How It Works HubSpot Trigger β Captures a new lead as soon as itβs created in HubSpot. Fetch Contact Data β Retrieves all relevant fields like name, company, and industry. Clean & Format Data β A Code node standardizes and structures the data for consistency. Airtable Record Creation β Logs the lead data into the βLeadsβ table for centralized tracking. AI Agent Qualification β The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update β Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification β Sends a real-time message tagging the rep with lead info. Gmail Notification β Sends a personalized handoff email with context and follow-up actions. HubSpot Sync β Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- π οΈ Setup Steps Trigger Node: HubSpot β Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the βLeadsβ table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- π Credentials Required HubSpot OAuth2 API β To fetch and update leads. Airtable Personal Access Token β To store and update lead data. OpenAI API β To power the AI qualification and matching logic. Slack OAuth2 β For sending team notifications. Gmail OAuth2 β For automatic email alerts to assigned reps, sir. --- π€ Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- π¬ Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular β perfect for scaling across global sales teams, sir.