Workflow Templates

Salesforce Lead Enrichment with Explorium Template Download the following json file and import it to a new n8n workflow: salesforce\_Workflow.json Overview This n8n workflow monitors your Salesforce instance for new leads and automatically enriches them with missing contact information. When a lead is created, the workflow: Detects the new lead via Salesforce trigger Matches the lead against Explorium's database using name and company Enriches the lead with professional email addresses and phone numbers Updates the Salesforce lead record with the discovered contact information This automation ensures your sales team always has the most up-to-date contact information for new leads, improving reach rates and accelerating the sales process. Key Features Real-time Processing**: Triggers automatically when new leads are created in Salesforce Intelligent Matching**: Uses lead name and company to find the correct person in Explorium's database Contact Enrichment**: Adds professional emails, mobile phones, and office phone numbers Batch Processing**: Efficiently handles multiple leads to optimize API usage Error Handling**: Continues processing other leads even if some fail to match Selective Updates**: Only updates leads that successfully match in Explorium Prerequisites Before setting up this workflow, ensure you have: n8n instance (self-hosted or cloud) Salesforce account with: OAuth2 API access enabled Lead object permissions (read/write) API usage limits available Explorium API credentials (Bearer token) - Get explorium api key Basic understanding of Salesforce lead management Salesforce Requirements Required Lead Fields The workflow expects these standard Salesforce lead fields: FirstName - Lead's first name LastName - Lead's last name Company - Company name Email - Will be populated/updated by the workflow Phone - Will be populated/updated by the workflow MobilePhone - Will be populated/updated by the workflow API Permissions Your Salesforce integration user needs: Read access to Lead object Write access to Lead object fields (Email, Phone, MobilePhone) API enabled on the user profile Sufficient API calls remaining in your org limits Installation & Setup Step 1: Import the Workflow Copy the workflow JSON from the template In n8n: Navigate to Workflows → Add Workflow → Import from File Paste the JSON and click Import Step 2: Configure Salesforce OAuth2 Credentials Click on the Salesforce Trigger node Under Credentials, click Create New Follow the OAuth2 flow: Client ID: From your Salesforce Connected App Client Secret: From your Salesforce Connected App Callback URL: Copy from n8n and add to your Connected App Authorize the connection Save the credentials as "Salesforce account connection" Note: Use the same credentials for all Salesforce nodes in the workflow. Step 3: Configure Explorium API Credentials Click on the Match\_prospect node Under Credentials, click Create New (HTTP Header Auth) Configure the header: Name: Authorization Value: Bearer YOUR_EXPLORIUM_API_TOKEN Save as "Header Auth account" Apply the same credentials to the Explorium Enrich Contacts Information node Step 4: Verify Node Settings Salesforce Trigger: Trigger On: Lead Created Poll Time: Every minute (adjust based on your needs) Salesforce Get Leads: Operation: Get All Condition: CreatedDate = TODAY (fetches today's leads) Limit: 20 (adjust based on volume) Loop Over Items: Batch Size: 6 (optimal for API rate limits) Step 5: Activate the Workflow Save the workflow Toggle the Active switch to ON The workflow will now monitor for new leads every minute Detailed Node Descriptions Salesforce Trigger: Polls Salesforce every minute for new leads Get Today's Leads: Retrieves all leads created today to ensure none are missed Loop Over Items: Processes leads in batches of 6 for efficiency Match Prospect: Searches Explorium for matching person using name + company Filter: Checks if a valid match was found Extract Prospect IDs: Collects all matched prospect IDs Enrich Contacts: Fetches detailed contact information from Explorium Merge: Combines original lead data with enrichment results Split Out: Separates individual enriched records Update Lead: Updates Salesforce with new contact information Data Mapping The workflow maps Explorium data to Salesforce fields as follows: | Explorium Field | Salesforce Field | Fallback Logic | | ------------------- | ---------------- | --------------------------------- | | emails[0].address | Email | Falls back to professions_email | | mobile_phone | MobilePhone | Falls back to phone_numbers[1] | | phone_numbers[0] | Phone | Falls back to mobile_phone | Usage & Monitoring Automatic Operation Once activated, the workflow runs automatically: Checks for new leads every minute Processes any leads created since the last check Updates leads with discovered contact information Continues running until deactivated Manual Testing To test the workflow manually: Create a test lead in Salesforce Click "Execute Workflow" in n8n Monitor the execution to see each step Verify the lead was updated in Salesforce Monitoring Executions Track workflow performance: Go to Executions in n8n Filter by this workflow Review successful and failed executions Check logs for any errors or issues Troubleshooting Common Issues No leads are being processed Verify the workflow is activated Check Salesforce API limits haven't been exceeded Ensure new leads have FirstName, LastName, and Company populated Confirm OAuth connection is still valid Leads not matching in Explorium Verify company names are accurate (not abbreviations) Check that first and last names are properly formatted Some individuals may not be in Explorium's database Try testing with known companies/contacts Contact information not updating Check Salesforce field-level security Verify the integration user has edit permissions Ensure Email, Phone, and MobilePhone fields are writeable Check for validation rules blocking updates Authentication errors Salesforce: Re-authorize OAuth connection Explorium: Verify Bearer token is valid and not expired Check API quotas haven't been exceeded Error Handling The workflow includes built-in error handling: Failed matches don't stop other leads from processing Each batch is processed independently Failed executions are logged for review Partial successes are possible (some leads updated, others skipped) Best Practices Data Quality Ensure complete lead data: FirstName, LastName, and Company should be populated Use full company names: "Microsoft Corporation" matches better than "MSFT" Standardize data entry: Consistent formatting improves match rates Performance Optimization Adjust batch size: Lower if hitting API limits, higher for efficiency Modify polling frequency: Every minute for high volume, less frequent for lower volume Set appropriate limits: Balance between processing speed and API usage Compliance & Privacy Data permissions: Ensure you have rights to enrich lead data GDPR compliance: Consider privacy regulations in your region Data retention: Follow your organization's data policies Audit trail: Monitor who has access to enriched data Customization Options Extend the Enrichment Add more Explorium enrichment by: Adding firmographic data (company size, revenue) Including technographic information Appending social media profiles Adding job title and department verification Modify Trigger Conditions Change when enrichment occurs: Trigger on lead updates (not just creation) Add specific lead source filters Process only leads from certain campaigns Include lead score thresholds Add Notifications Enhance with alerts: Email sales reps when leads are enriched Send Slack notifications for high-value matches Create tasks for leads that couldn't be enriched Log enrichment metrics to dashboards API Considerations Salesforce Limits API calls: Each execution uses \~4 Salesforce API calls Polling frequency: Consider your daily API limit Batch processing: Reduces API usage vs. individual processing Explorium Limits Match API: One call per batch of leads Enrichment API: One call per batch of matched prospects Rate limits: Respect your plan's requests per minute Integration Architecture This workflow can be part of a larger lead management system: Lead Capture → This Workflow → Lead Scoring → Assignment Can trigger additional workflows based on enrichment results Compatible with existing Salesforce automation (Process Builder, Flows) Works alongside other enrichment tools Security Considerations Credentials**: Stored securely in n8n's credential system Data transmission**: Uses HTTPS for all API calls Access control**: Limit who can modify the workflow Audit logging**: All executions are logged with details Support Resources For assistance with: n8n issues**: Consult n8n documentation or community forum Salesforce integration**: Reference Salesforce API documentation Explorium API**: Contact Explorium support for API questions Workflow logic**: Review execution logs for debugging

🎬 TikTok Influencer Scraper (URL Input) via Bright Data + n8n & Sheets A comprehensive n8n automation that scrapes TikTok influencer profiles using Bright Data's TikTok dataset and automatically saves detailed profile information to Google Sheets. 📋 Overview This workflow provides an automated TikTok influencer data collection solution that scrapes comprehensive profile information and saves it to Google Sheets. Perfect for influencer marketing research, competitor analysis, social media monitoring, and marketing campaign planning. ✨ Key Features 📝 Form-Based Input: Simple web form to submit TikTok profile URLs 🤖 Bright Data Integration: Uses Bright Data's TikTok dataset for reliable scraping ⏳ Status Monitoring: Intelligent polling system to check scraping progress 🔄 Retry Logic: Automatic retry mechanism with 30-second intervals 📊 Data Extraction: Comprehensive profile data including engagement metrics 📈 Google Sheets Storage: Automatic data storage and organization ⚡ Error Handling: Built-in error handling and status reporting 🎯 Custom Fields: Configurable output fields for specific data needs 🎯 What This Workflow Does Input Profile URLs**: TikTok profile URLs submitted through web form Custom Fields**: Configurable data fields for extraction Country Settings**: Geo-targeting for accurate data collection Processing Form Submission: User submits TikTok profile URL through web form API Trigger: Sends profile data to Bright Data for scraping Status Polling: Continuously checks scraping progress Wait & Retry: Implements 30-second delays between status checks Data Retrieval: Fetches complete profile data when ready Sheet Update: Saves extracted data to Google Sheets Status Reporting: Provides completion status and messages Output Data Points | Field | Description | Example | |-------|-------------|---------| | Account ID | Unique TikTok account identifier | @username123 | | Nickname | Display name on profile | "John Doe" | | Biography | Profile bio/description | "Content creator & influencer" | | Followers | Number of followers | 1,250,000 | | Following | Number of accounts following | 500 | | Likes | Total likes across all videos | 50,000,000 | | Videos Count | Total number of videos posted | 1,200 | | Profile URL | Direct link to TikTok profile | https://www.tiktok.com/@username | | Profile Picture | Profile image URL | https://p16-sign-sg.tiktokcdn.com/... | | Profile Picture HD | High-definition profile image | https://p16-sign-sg.tiktokcdn.com/... | | Is Verified | Verification status | true/false | | Bio Link | External link in bio | https://linktr.ee/username | | Like Engagement Rate | Engagement rate based on likes | 5.2% | | Comment Engagement Rate | Engagement rate based on comments | 2.1% | | Top Videos | List of top performing videos | [video_objects] | | Region | Geographic region | "US" | | Is Under Age 18 | Age status indicator | true/false | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with TikTok dataset access Valid TikTok profile URLs for testing 10-15 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials: In n8n: Credentials → + Add credential → HTTP Request Generic Credential Name: "Bright Data API" Authentication: Bearer Token Token: Your Bright Data API key Test the connection Configure dataset: Ensure you have access to TikTok dataset (gd_l1villgoiiidt09ci) Verify dataset permissions in Bright Data dashboard Check dataset limits and pricing Step 3: Configure Google Sheets Integration Create a Google Sheet: Go to Google Sheets Create a new spreadsheet named "TikTok Influencer Data" Create a sheet tab named "TikTok profile by url" Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Set up Google Sheets credentials: In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Prepare your data sheet with columns: Column A: Account ID Column B: Nickname Column C: Biography Column D: Followers Column E: Following Column F: Likes Column G: Videos Count Column H: Profile URL Column I: Is Verified Column J: Bio Link Column K: Like Engagement Rate Column L: Comment Engagement Rate Column M: Region Column N: Status Column O: Message Step 4: Update Workflow Settings Update API credentials: Open "Sends profile URLs to Bright Data to trigger scraping" node Replace BRIGHT_DATA_API_KEY with your actual API key Update dataset ID if different Update Google Sheets nodes: Open "Google Sheets" node Replace document ID: 1OeqtCFm4Wek9DI5YFOWQXTpQJS-SJxC10iAPKEKkmiY Select your Google Sheets credential Choose the correct sheet/tab name Configure form settings: Open "Search by Profile URL" node Customize form title and field labels as needed Note the webhook URL for form access Step 5: Test & Activate Add test profiles: Access the form using the webhook URL Submit 1-2 TikTok profile URLs for testing Use full URLs (e.g., https://www.tiktok.com/@username) Test the workflow: Submit a test profile through the form Monitor execution in n8n Verify data appears in Google Sheet Check for any error messages 📖 Usage Guide Submitting TikTok Profiles Navigate to your form URL (found in Form Trigger node) Enter TikTok profile URL in the format: https://www.tiktok.com/@username Click Submit to start the scraping process Wait for processing (typically 1-3 minutes) Understanding the Process The workflow follows this sequence: Form Submission → Profile URL captured API Trigger → Scraping job submitted to Bright Data Status Polling → Checks every 30 seconds if data is ready Data Retrieval → Fetches complete profile information Sheet Update → Saves data to Google Sheets Monitoring Progress Check n8n execution logs for real-time status Bright Data dashboard shows scraping progress Google Sheets will populate when data is ready Status column shows "ready" when complete Reading the Results Your Google Sheet will show: Complete TikTok profile information Engagement metrics and statistics Profile verification status Bio links and external connections Timestamp of data collection 🔧 Customization Options Adding More Data Points Edit the JSON body in "Sends profile URLs to Bright Data" node to include additional fields: "custom_output_fields": [ "account_id", "nickname", "biography", "followers", "following", "likes", "videos_count", "language", "creation_time", "last_post_time", "avg_video_duration", "hashtags_used", "music_used" ] Modifying Input Parameters Customize the scraping parameters: Country targeting**: Change "country" field in input Search limits**: Adjust "limit_per_input" value Discovery method**: Modify "discover_by" parameter Error handling**: Toggle "include_errors" setting Batch Processing Multiple Profiles To process multiple profiles simultaneously: Modify the input array in the API call Add multiple profile URLs in single request Implement loop logic for processing results Add rate limiting between requests Custom Form Fields Enhance the form with additional inputs: Open "Search by Profile URL" node Add form fields for: Country selection Number of videos to analyze Specific date ranges Custom tags or categories 🚨 Troubleshooting Common Issues & Solutions "Bright Data connection failed" Cause: Invalid API credentials or dataset access Solution: Verify API key in Bright Data dashboard, check dataset permissions "Profile not found or private" Cause: Invalid TikTok URL or private profile Solution: Verify profile URL format, ensure profile is public "Google Sheets permission denied" Cause: Incorrect credentials or sheet permissions Solution: Re-authenticate Google Sheets, check sheet sharing settings "Scraping timeout" Cause: Profile data taking too long to process Solution: Increase wait time or implement longer polling intervals "Invalid dataset ID" Cause: Incorrect or expired dataset configuration Solution: Check Bright Data dashboard for correct dataset ID "Form submission failed" Cause: Webhook configuration issues Solution: Verify webhook URL and form trigger settings Advanced Troubleshooting Check execution logs** in n8n for detailed error messages Test individual nodes** by running them separately Verify data formats** ensure URLs are properly formatted Monitor API limits** check Bright Data usage quotas Add error handling** implement try-catch logic for robust operation 📊 Use Cases & Examples 1. Influencer Marketing Research Goal: Identify and analyze potential influencers for campaigns Research influencers in specific niches Analyze engagement rates and audience size Compare multiple influencers for campaign selection Track influencer growth over time 2. Competitive Analysis Goal: Monitor competitors' TikTok presence and performance Track competitor follower growth Analyze content strategies and engagement Monitor posting frequency and timing Identify trending content themes 3. Social Media Monitoring Goal: Track brand mentions and user-generated content Monitor branded hashtag usage Track brand advocates and micro-influencers Analyze sentiment and engagement patterns Identify trending topics in your industry 4. Market Research Pipeline Goal: Gather social media intelligence for business decisions Analyze target audience behavior Study content preferences and trends Generate reports for stakeholders Support marketing strategy development ⚙ Advanced Configuration Rate Limiting and Performance To optimize for large-scale scraping: Adjust wait times between status checks Implement exponential backoff for retries Add batch processing for multiple profiles Monitor API usage to avoid limits Data Validation and Cleaning Enhance data quality with validation: Add data type validation for numeric fields Implement URL format checking Clean and standardize text fields Add data completeness checks Integration with Business Tools Connect the workflow to your existing systems: CRM Integration**: Update customer records with influencer data Slack Notifications**: Send alerts when new data is available Database Storage**: Store data in PostgreSQL/MySQL for analysis BI Tools**: Connect to Tableau/Power BI for visualization Webhook Integration For real-time updates: Add webhook triggers for immediate profile checks Integrate with external systems via webhooks Create API endpoints for programmatic access Implement authentication for secure access 📈 Performance & Limits Expected Performance Single Profile**: 30-60 seconds average processing time Concurrent Requests**: 5-10 simultaneous (depends on Bright Data plan) Data Accuracy**: 95%+ for public TikTok profiles Success Rate**: 90%+ for accessible profiles Daily Capacity**: 100-1000 profiles (depends on rate limits) Resource Usage Memory**: ~50MB per execution Storage**: Minimal (data stored in Google Sheets) API Calls**: 3-5 Bright Data calls per profile (including status checks) Bandwidth**: ~1-2MB per profile scraped Execution Time**: 1-2 minutes per profile Scaling Considerations Rate Limiting**: Add delays for high-volume scraping Error Handling**: Implement retry logic for failed requests Data Validation**: Add checks for malformed profile data Monitoring**: Track success/failure rates over time Cost Optimization**: Monitor API usage to control costs 🤝 Support & Community Getting Help n8n Community Forum**: community.n8n.io Documentation**: docs.n8n.io Bright Data Support**: Contact through your dashboard GitHub Issues**: Report bugs and feature requests Contributing Share improvements with the community Report issues and suggest enhancements Create variations for specific use cases Document best practices and lessons learned 📋 Quick Setup Checklist Before You Start ☐ n8n instance running (self-hosted or cloud) ☐ Google account with Sheets access ☐ Bright Data account with TikTok dataset access ☐ Valid TikTok profile URLs for testing ☐ 15 minutes for setup Setup Steps ☐ Import Workflow - Copy JSON and import to n8n ☐ Configure Bright Data - Set up API credentials and test ☐ Create Google Sheet - New sheet with proper column structure ☐ Set up Google Sheets credentials - OAuth setup and test ☐ Update workflow settings - Replace sheet ID and API keys ☐ Test with sample profiles - Submit 1-2 URLs and verify results ☐ Activate workflow - Enable form trigger for production use Ready to Use! 🎉 Your form URL: https://your-n8n-instance.com/form/[webhook-id] 🎯 Happy TikTok Scraping! This workflow provides a solid foundation for automated TikTok influencer data collection. Customize it to fit your specific needs and use cases for influencer marketing, competitive analysis, and social media research.

Who is this for? This workflow is ideal for IT operations teams or system administrators who use ServiceNow to track incidents and Slack for team communication. It provides real-time updates on new ServiceNow incidents directly in a designated Slack channel, ensuring timely response and collaboration. What problem is this workflow solving? / Use case Manually monitoring ServiceNow for new incidents can be time-consuming and prone to delays. This workflow automates the process, ensuring that team members are instantly notified of new incidents, complete with all relevant details, in a Slack channel. It enhances operational efficiency and incident response time. What this workflow does Schedule or Manual Trigger: The workflow can be triggered manually or set to run automatically every 5 minutes. Retrieve New Incidents: Fetches incidents created in ServiceNow within the last 5 minutes. Error Handling: Posts an error message in Slack if there are issues connecting to ServiceNow. Incident Processing: If new incidents are found, they are sorted in ascending order by their number. Detailed incident information is formatted and sent to a specified Slack channel. No Incidents: If no new incidents are found, the workflow does nothing. Setup ServiceNow API Credentials: Configure ServiceNow Basic Authentication in the workflow to connect to your ServiceNow instance. Slack API Credentials: Add your Slack API credentials to enable message posting. Slack Channel Configuration: Define the Slack channel where notifications should be sent. Ensure the channel ID is correctly set in the Slack node. Adjust the Schedule: Modify the schedule in the Schedule Trigger node to suit your requirements. How to customize this workflow to your needs Notification Format: Customize the Slack message format to include additional or fewer details. Update the Blocks section in the Slack node for personalized messages. Incident Query Parameters: Adjust the sysparm_query parameter in the ServiceNow node to filter incidents based on specific criteria. Error Handling: Modify the error message in the Slack node for more detailed troubleshooting information. Features Real-Time Notifications**: Immediate updates on new ServiceNow incidents. Error Handling**: Alerts in Slack if the workflow encounters issues connecting to ServiceNow. Customizable Notifications**: Flexibility to modify incident details sent to Slack. This workflow streamlines incident management and fosters collaboration by delivering actionable updates directly to your team.

Workflow Documentation: Auto-Retry Engine – Error Recovery Workflow Detailed Description The Auto-Retry Engine: Error Recovery Workflow is designed to automate the process of identifying and retrying failed executions in n8n workflows. By leveraging scheduled triggers, API integrations, and conditional logic, this workflow ensures that any failed executions are automatically retried on an hourly basis. This reduces manual intervention, improves system reliability, and ensures smoother workflow operations. Who is this for? This workflow is ideal for: Automation Engineers**: Managing and maintaining workflows with minimal manual intervention. DevOps Teams**: Ensuring high availability and reliability of automated processes. IT Administrators**: Reducing downtime and improving system performance by automating error recovery. What problem does this workflow solve? Manual Error Handling**: Eliminates the need for manual monitoring and retrying of failed executions. Improved Reliability**: Automatically retries failed executions, reducing downtime and improving workflow success rates. Time Efficiency**: Saves time by automating repetitive error recovery tasks, allowing teams to focus on higher-priority work. What this workflow does This workflow automates the following steps: Scheduled Monitoring: Checks for failed executions hourly using a schedule trigger. Error Filtering: Identifies executions that have failed and filters out those that have already been successfully retried. Authentication: Logs into the n8n instance using API credentials to retrieve session details. Automatic Retry: Retries the failed executions using the n8n API. Batch Processing: Processes multiple failed executions in batches to avoid overloading the system. Setup Prerequisites To use this workflow, you’ll need: n8n Account**: To create and run the workflow. n8n API Credentials**: For logging into the n8n instance and retrying executions. HTTP Request Node**: Configured to interact with the n8n API. Schedule Trigger**: Set to run the workflow hourly. Setup Process Configure Schedule Trigger Set the trigger to run hourly to check for failed executions. Set Login Credentials Add your n8n instance URL, username, and password in the Set Node. Integrate n8n API Use the HTTP Request node to log into the n8n instance and retrieve session details. Retry Failed Executions Configure the HTTP Request node to retry failed executions using the session details. Batch Processing Use the Split in Batches node to process multiple failed executions in batches. How to customize this workflow Tailor the workflow to fit your specific needs: Adjust Schedule Frequency** Modify the schedule trigger to run at different intervals (e.g., every 30 minutes). Add Notifications** Integrate email or Slack notifications to alert teams about failed retries. Refine Error Filtering** Customize the filtering logic to exclude specific types of failed executions. Scale Batch Size** Adjust the batch size in the Split in Batches node to optimize performance. Conclusion The Auto-Retry Engine: Error Recovery Workflow is a powerful tool for automating error recovery in n8n workflows. By reducing manual intervention and ensuring failed executions are retried automatically, this workflow enhances system reliability and operational efficiency. Whether you're managing a few workflows or a complex automation ecosystem, this workflow ensures your processes run smoothly and consistently.

Monitor BeyondPresence video agent conversations in real-time to automatically score leads (0-100+) based on buying signals and send instant Slack alerts when hot opportunities or competitors are mentioned. This template helps sales teams prioritize leads immediately, never miss competitor mentions, and respond to high-intent prospects while they're still engaged. How it works Real-time webhook** processes each user message as it happens during calls Scoring engine** analyzes for buying signals (+points) and objections (-points) Competitor detection** instantly identifies when alternatives are mentioned Smart routing** sends alerts to different Slack channels based on urgency Hot leads** (70+ score) trigger immediate notifications with recommendations Call summary (Optional)** provides final qualification score when conversation ends Set up steps Connect Slack OAuth2 - Use n8n's built-in Slack integration (no webhooks needed!) Create Slack channels - Set up #sales-hot-leads, #sales-competitors, #sales-qualified Add webhook to BeyondPresence - Copy URL from n8n to BeyondPresence Settings → Webhooks Customize competitors - Edit the scoring node to add your specific competitor names Adjust scoring weights (optional) - Tune point values for your sales process Setup time: 10-15 minutes Requirements: BeyondPresence account, Slack workspace admin access

Objective In industry and production sometimes machine data is available in databases. That might be sensor data like temperature or pressure or just binary information. In this sample flow reads machine data and sends an alert to your SIGNL4 team when the machine is down. When the machine is up again the alert in SIGNL4 will get closed automatically. Setup We simulate the machine data using a Notion table. When we un-check the Up box we simulate a machine-down event. In certain intervals n8n checks the database for down items. If such an item has been found an alert is send using SIGNL4 and the item in Notion is updates (in order not to read it again). Status updates from SIGNL4 (acknowledgement, close, annotation, escalation, etc.) are received via webhook and we update the Notion item accordingly. This is how the alert looks like in the SIGNL4 app. The flow can be easily adapted to other database monitoring scenarios.

HubSpot Contact Enrichment with Explorium Template Download the following json file and import it to a new n8n workflow: hubspot\_flow.json Overview This n8n workflow monitors your HubSpot instance for newly created contacts and automatically enriches them with additional contact information. When a contact is created, the workflow: Detects the new contact via HubSpot webhook trigger Retrieves recent contact details from HubSpot Matches the contact against Explorium's database using name, company, and email Enriches the contact with professional emails and phone numbers Updates the HubSpot contact record with discovered information This automation ensures your sales and marketing teams have complete contact information, improving outreach success rates and data quality. Key Features Real-time Webhook Trigger**: Instantly processes new contacts as they're created Intelligent Matching**: Uses multiple data points (name, company, email) for accurate matching Comprehensive Enrichment**: Adds both professional and work emails, plus phone numbers Batch Processing**: Efficiently handles multiple contacts to optimize API usage Smart Data Mapping**: Intelligently maps multiple emails and phone numbers Profile Enrichment**: Optional additional enrichment for deeper contact insights Error Resilience**: Continues processing other contacts if some fail to match Prerequisites Before setting up this workflow, ensure you have: n8n instance (self-hosted or cloud) HubSpot account with: Developer API access (for webhooks) Private App or OAuth2 app created Contact object permissions (read/write) Explorium API credentials (Bearer token) - Get explorium api key Understanding of HubSpot contact properties HubSpot Requirements Required Contact Properties The workflow uses these HubSpot contact properties: firstname - Contact's first name lastname - Contact's last name company - Associated company name email - Primary email (read and updated) work_email - Work email (updated by workflow) phone - Phone number (updated by workflow) API Access Setup Create a Private App in HubSpot: Navigate to Settings → Integrations → Private Apps Create new app with Contact read/write scopes Copy the Access Token Set up Webhooks (for Developer API): Create app in HubSpot Developers portal Configure webhook for contact.creation events Note the App ID and Developer API Key Custom Properties (Optional) Consider creating custom properties for: Multiple email addresses Mobile vs. office phone numbers Data enrichment timestamps Match confidence scores Installation & Setup Step 1: Import the Workflow Copy the workflow JSON from the template In n8n: Navigate to Workflows → Add Workflow → Import from File Paste the JSON and click Import Step 2: Configure HubSpot Developer API (Webhook) Click on the HubSpot Trigger node Under Credentials, click Create New Enter your HubSpot Developer credentials: App ID: From your HubSpot app Developer API Key: From your developer account Client Secret: From your app settings Save as "HubSpot Developer account" Step 3: Configure HubSpot App Token Click on the HubSpot Recently Created node Under Credentials, click Create New (App Token) Enter your Private App access token Save as "HubSpot App Token account" Apply the same credentials to the Update HubSpot node Step 4: Configure Explorium API Credentials Click on the Explorium Match Prospects node Under Credentials, click Create New (HTTP Header Auth) Configure the authentication: Name: Authorization Value: Bearer YOUR_EXPLORIUM_API_TOKEN Save as "Header Auth Connection" Apply to all Explorium nodes: Explorium Enrich Contacts Information Explorium Enrich Profiles Step 5: Configure Webhook Subscription In HubSpot Developers portal: Go to your app's webhook settings Add subscription for contact.creation events Set the target URL from the HubSpot Trigger node Activate the subscription Step 6: Activate the Workflow Save the workflow Toggle the Active switch to ON The webhook is now listening for new contacts Node Descriptions HubSpot Trigger: Webhook that fires when new contacts are created HubSpot Recently Created: Fetches details of recently created contacts Loop Over Items: Processes contacts in batches of 6 Explorium Match Prospects: Finds matching person in Explorium database Filter: Validates successful matches Extract Prospect IDs: Collects matched prospect identifiers Enrich Contacts Information: Fetches emails and phone numbers Enrich Profiles: Gets additional profile data (optional) Merge: Combines all enrichment results Split Out: Separates individual enriched records Update HubSpot: Updates contact with new information Data Mapping Logic The workflow maps Explorium data to HubSpot properties: | Explorium Data | HubSpot Property | Notes | | ------------------------------ | ------------------ | ----------------------------- | | professions_email | email | Primary professional email | | emails[].address | work_email | All email addresses joined | | phone_numbers[].phone_number | phone | All phones joined with commas | | mobile_phone | phone (fallback) | Used if no other phones found | Data Processing The workflow handles complex data scenarios: Multiple emails**: Joins all discovered emails with commas Phone numbers**: Combines all phone numbers into a single field Missing data**: Uses "null" as placeholder for empty fields Name parsing**: Cleans sample data and special characters Usage & Operation Automatic Processing Once activated: Every new contact triggers the webhook immediately Contact is enriched within seconds HubSpot record is updated automatically Process repeats for each new contact Manual Testing To test the workflow: Use the pinned test data in the HubSpot Trigger node, or Create a test contact in HubSpot Monitor the execution in n8n Verify the contact was updated in HubSpot Monitoring Performance Track workflow health: Go to Executions in n8n Filter by this workflow Monitor success rates Review any failed executions Check webhook delivery in HubSpot Troubleshooting Common Issues Webhook not triggering Verify webhook subscription is active in HubSpot Check the webhook URL is correct and accessible Ensure workflow is activated in n8n Test webhook delivery in HubSpot developers portal Contacts not matching Verify contact has firstname, lastname, and company Check for typos or abbreviations in company names Some individuals may not be in Explorium's database Email matching improves accuracy significantly Updates failing in HubSpot Check API token has contact write permissions Verify property names exist in HubSpot Ensure rate limits haven't been exceeded Check for validation rules on properties Missing enrichment data Not all prospects have all data types Phone numbers may be less available than emails Profile enrichment is optional and may not always return data Error Handling Built-in error resilience: Failed matches don't block other contacts Each batch processes independently Partial enrichment is possible All errors are logged for review Debugging Tips Check webhook logs: HubSpot shows delivery attempts Review executions: n8n logs show detailed error messages Test with pinned data: Use the sample data for isolated testing Verify API responses: Check Explorium API returns expected data Best Practices Data Quality Complete contact records: Ensure name and company are populated Standardize company names: Use official names, not abbreviations Include existing emails: Improves match accuracy Regular data hygiene: Clean up test and invalid contacts Performance Optimization Batch size: 6 is optimal for rate limits Webhook reliability: Monitor delivery success API quotas: Track usage in both platforms Execution history: Regularly clean old executions Compliance & Privacy GDPR compliance: Ensure lawful basis for enrichment Data minimization: Only enrich necessary fields Access controls: Limit who can modify enriched data Audit trail: Document enrichment for compliance Customization Options Additional Enrichment Extend with more Explorium data: Job titles and departments Social media profiles Professional experience Skills and interests Company information Enhanced Processing Add workflow logic for: Lead scoring based on enrichment Routing based on data quality Notifications for high-value matches Custom field mapping Integration Extensions Connect to other systems: Sync enriched data to CRM Trigger marketing automation Update data warehouse Send notifications to Slack API Considerations HubSpot Limits API calls**: Monitor daily limits Webhook payload**: Max 200 contacts per trigger Rate limits**: 100 requests per 10 seconds Property limits**: Max 1000 custom properties Explorium Limits Match API**: Batched for efficiency Enrichment calls**: Two parallel enrichments Rate limits**: Based on your plan Data freshness**: Real-time matching Architecture Considerations This workflow integrates with: HubSpot workflows and automation Marketing campaigns and sequences Sales engagement tools Reporting and analytics Other enrichment services Security Best Practices Webhook validation**: Verify requests are from HubSpot Token security**: Rotate API tokens regularly Access control**: Limit workflow modifications Data encryption**: All API calls use HTTPS Audit logging**: Track all enrichments Advanced Configuration Custom Field Mapping Modify the Update HubSpot node to map to custom properties: // Example custom mapping { "custom_mobile": "{{ $json.data.mobile_phone }}", "custom_linkedin": "{{ $json.data.linkedin_url }}", "enrichment_date": "{{ $now.toISO() }}" } Conditional Processing Add logic to process only certain contacts: Filter by contact source Check for specific properties Validate email domains Exclude test contacts Support Resources For assistance: n8n issues**: Check n8n documentation and forums HubSpot API**: Reference HubSpot developers documentation Explorium API**: Contact Explorium support Webhook issues**: Use HubSpot webhook testing tools

Quick overview This workflow receives a manuscript’s reference list via a header-authenticated webhook, checks each citation against Crossref (including Retraction Watch data) and doi.org, uses OpenAI to draft editor-facing integrity memos and author queries for flagged items, logs outcomes in n8n Data Tables, and re-checks accepted manuscripts weekly. How it works Receives a POST request with a manuscript ID, submitter email, and references list through a header-protected webhook. Validates and normalizes the input, de-duplicates references, and reuses a recent closed result or returns an already-open case for the same manuscript when applicable. Looks up each reference in the Crossref API (or runs a Crossref bibliographic search when no DOI is provided) and flags issues such as retractions, expressions of concern, corrections, DOI/citation mismatches, duplicates, or unresolved references. For DOIs missing from Crossref, queries the doi.org handle API to distinguish non-Crossref registrations from non-existent (potentially fabricated) DOIs. Aggregates all flags into an overall verdict floor and, when review is needed, uses an OpenAI agent (with Crossref and OpenAlex lookups) to produce a structured memo and draft author queries, then emails the report to the editor in Gmail. Collects the editor’s decision via a form (optionally requiring integrity-officer approval to keep blocking references), writes the final outcome to n8n Data Tables, notifies the submitter by Gmail, and adds accepted/auto-cleared manuscripts to a watch list. Runs every Monday to re-check watched DOIs in Crossref and reopens a new “post_publication_alert” check via webhook when a cited work’s status worsens. Setup Run the manual “Run once to set up” path to create the n8n Data Tables named reference_checks and reference_watch. Create and attach an HTTP Header Auth credential for the intake webhook and for the “reopen check” HTTP request used by weekly monitoring. Add OpenAI credentials (for the agent) and Gmail credentials (for editor notifications, submitter messages, integrity-officer approvals, and error alerts). Replace placeholder values in “Reference check settings” and “Monitoring settings” (editor email, integrity officer email, Crossref mailto, ops email, and the workflow’s production webhook URL for reopening checks). Requirements A current n8n version with Data Tables and the AI Agent node (cloud or self-hosted). An OpenAI API key, gpt-4.1-mini by default. The model is called only for lists with a medium or high flag. A Gmail account. You can swap in Outlook, Slack or Teams for the notifications. No other keys: Crossref, doi.org and OpenAlex are free public APIs. Crossref needs a real contact e-mail in crossref_mailto (polite pool); the workflow refuses to run with the placeholder. OpenAlex single-DOI lookups need no key. Customization Matching: title_match_threshold and mismatch_threshold in Reference check settings, plus STOP words in Read Crossref results. Severities and actions: edit them in Read Crossref results and Read doi.org result. For example, make CORRECTED medium for clinical journals. Reuse and open-check windows: REUSE_DAYS and OPEN_CHECK_DAYS in Check earlier checks and reuse window. Follow-ups and blocking codes: MAX_FOLLOW_UPS and BLOCKING_CODES in Apply review policy. Timeouts are set in Wait for editor decision and Ask integrity officer to approve. Weekly volume: MAX_DOIS in Collect DOIs to re-check. Model: replace OpenAI with Anthropic, Gemini or a self-hosted model through Ollama. Additional info Limitations: Only works with a DOI, or with a citation that matches Crossref confidently, are checked; books, laws and web pages without a DOI usually end up UNRESOLVED for a person to verify. Retraction data is as complete as Crossref and Retraction Watch; a missing flag does not prove a cited work is sound. DOIs registered outside Crossref (DataCite, mEDRA) are reported as NOT_IN_CROSSREF and are not checked for retractions. METADATA_MISMATCH compares title words and year only, so it is medium, not high. Reference lists and citing context (no full text) are sent to the AI provider you connect. Decision support only: the editor decides.

Quick overview This workflow runs daily to scan open Salesforce opportunities and related tasks, detect overdue or high-priority internal dependencies, generate an AI-written escalation summary with Google Gemini, and then notify stakeholders via Slack and Gmail while updating the opportunity and logging the incident to Google Sheets. How it works Runs every day at 9:00 to start a bottleneck check. Retrieves all open Salesforce opportunities and Salesforce tasks linked to records, then matches tasks to their related opportunities. Flags tasks as bottlenecks when they are overdue or high priority and still open, assigns a dependency type from the task subject (for example legal, proposal, technical, or finance), and calculates a severity level. For each detected bottleneck, sends the opportunity and task context to Google Gemini to produce a JSON summary, escalation reason, recommended next action, owner team, and severity. Sends a formatted bottleneck alert to a Slack channel and a plain-text escalation email via Gmail. Updates the Salesforce opportunity Next Step field with the bottleneck details and appends an audit row to a Google Sheets log. Setup Add Salesforce credentials with permission to query Opportunities and Tasks and to update Opportunities. Add Google Gemini credentials for the chat model used to generate bottleneck summaries. Add a Slack OAuth2 connection and select the channel where bottleneck alerts should be posted. Add a Gmail connection and configure the recipient(s) for escalation emails in the Gmail node. Add Google Sheets credentials and set the target spreadsheet and sheet tab used to append bottleneck audit rows. Additional info How To Customize Nodes Detection Logic (Code Node)** Modify conditions for bottlenecks (e.g., include medium priority or custom SLA rules) Dependency Classification** Update keyword matching logic to suit your business processes AI Prompt** Customize instructions to refine outputs (e.g., tone, structure, or additional fields) Slack Message Format** Adjust formatting or include/exclude fields based on team needs Salesforce Update Field** Modify which field is updated (currently "Next Step") Google Sheets Columns** Add/remove columns based on reporting requirements Add-ons & Enhancements Add Google Tasks for follow-up tracking Introduce retry logic for failed Slack/Gmail sends Add deduplication to avoid repeated alerts Include escalation workflows (e.g., manager alerts) Add dashboards using BI tools connected to Google Sheets Use Case Examples Detect overdue legal reviews blocking deal closure Identify high-priority proposal approvals not completed Track finance approvals delaying revenue recognition Alert teams about technical dependencies in late-stage deals Maintain an audit trail of all escalated bottlenecks There can be many more use cases depending on your internal processes. Troubleshooting Guide | Issue | Possible Cause | Solution | | ----------------------- | -------------------------------- | ----------------------------------------------- | | No data returned | Salesforce query issue | Verify SOQL queries and permissions | | AI output parsing fails | Invalid JSON from AI | Check prompt formatting and enforce strict JSON | | Slack message not sent | Incorrect credentials or channel | Reconnect Slack and verify channel ID | | Email not delivered | Gmail config issue | Check Gmail OAuth and recipient field | | Salesforce update fails | Field length limit | Ensure text is within 255 characters | | Missing bottlenecks | Logic too strict | Adjust detection conditions in Code node | Need Help? If you need assistance setting up this workflow, customizing it for your business or building advanced automation solutions, feel free to reach out. Contact WeblineIndia for: Workflow setup and deployment Custom AI automation solutions Salesforce and CRM integrations Advanced n8n workflow development We are happy to help you scale your automation journey.

Quick overview This workflow runs daily to compare procurement transactions in Google Sheets against active contract terms stored in Notion, flags pricing/SLA/volume violations with calculated leakage, and uses Groq LLM analysis to generate corrective recommendations that are logged back to Google Sheets. How it works Runs every day at 6:00 AM on a schedule. Retrieves all active contract records from a Notion database. Pulls recent procurement transactions from Google Sheets and compares each transaction to the matching contract SKU to detect pricing overcharges, SLA breaches, volume threshold issues, or missing contracts, calculating per-transaction leakage. Appends compliant transactions to a dedicated Google Sheets tab for audit tracking. For violations, loads historical violation logs from Google Sheets and calculates vendor/SKU recurrence counts and cumulative leakage to estimate total impact. Processes each current violation through Groq Chat to produce a structured impact assessment, urgency, and corrective recommendation. Appends the violation details, calculated leakage, and AI recommendation to the Google Sheets “Violation Logs” audit sheet. Setup Add Notion credentials and select the Notion database that contains your contract records, including fields for SKU, agreed price, SLA days, volume threshold, contract ID, and an “Active” status. Add Google Sheets credentials and set the spreadsheet and sheet tabs used for input transactions, compliant transaction logging, and violation logging. Add a Groq API credential for the LangChain Groq Chat model used to generate structured recommendations. Ensure your Google Sheets transaction columns match the workflow’s expected headers (for example: Transaction ID, Date, Vendor, Item SKU, Quantity Purchased, Unit Price Paid, and Actual Delivery Days). Additional info How To Customize Nodes Adjusting Violation Thresholds:** Open the Compare Terms vs Actuals node. You can modify the JavaScript to include a tolerance buffer (e.g., only flagging price overcharges if they exceed the contracted rate by more than 2%). Customizing AI Output:** The Enforce JSON Schema node strictly structures the AI's response (Impact Analysis, Action Category, Urgency, Recommendation). You can add new schema fields here, such as a drafted email response to the vendor. Visual Canvas Organization:** You can improve workspace readability for your team by styling the sticky notes with low-saturation, sensory-friendly color palettes and utilizing dark charcoal text to clearly map out compliance routing paths without overwhelming the reader. Add‑ons To expand the functionality of this workflow, consider adding: Vendor Email Automation:** Add a Gmail or Outlook node after the AI Analysis to automatically email the vendor a "Notice of Contract Violation" based on the AI's recommendation. ERP Integration:** Replace Google Sheets with HTTP Request nodes to pull daily transactions and log compliance statuses directly into ERP systems like NetSuite or SAP Ariba. Ticketing System Sync:** Connect Jira or ServiceNow to automatically open a resolution ticket for any violation flagged with a "High" urgency by the AI. Use Case Examples This workflow handles multiple procurement scenarios simultaneously. Primary use cases include: Price Overcharge Detection:** A supplier invoices $12 per unit instead of the contracted $10. The workflow catches the discrepancy, calculates total leakage based on volume, and alerts the buyer to request a credit memo. SLA/Late Delivery Breach:** A critical shipment arrives in 14 days, but the contract mandates a maximum of 10 days. The workflow flags the SLA breach so procurement can enforce late-delivery penalties. Volume Threshold Exceeded:** A buyer accidentally orders 5,000 units on a contract capped at 2,000 units per month. The workflow detects the overage and advises management to renegotiate terms or hold the excess inventory. Repeat Offender Identification:** A vendor continually overcharges by small amounts over several months. The AI analyzes the historical logs, identifies the recurring pattern, calculates cumulative impact, and recommends a systemic supplier review. (There can be many more variations of this workflow by simply adjusting the JavaScript variance rules or adding additional AI evaluation criteria!) Troubleshooting Guide | Issue | Possible Cause | Solution | | --- | --- | --- | | Workflow misses contract matches | Naming discrepancies between Notion and Google Sheets. | Check for trailing spaces or case-sensitivity issues. The Compare Terms vs Actuals node requires an exact text match on the SKU fields. | | Code node returns NaN for leakage | Quantity or Price fields in Google Sheets are formatted as text. | Ensure numeric columns in Google Sheets are strictly formatted as numbers, or update the JS code to parse strings into floats. | | AI Node times out or throws schema errors | Groq API rate limits reached or unexpected LLM output. | Verify your Groq API credentials. If the model fails to parse JSON, consider switching to a different supported model like Llama 3 within the Groq node. | | Slack alerts are not sending | Incorrect Channel ID or missing app permissions. | Re-authenticate the Slack node and ensure the n8n bot is invited to the target channel. | Need Help? Building reliable, automated compliance engines requires a deep understanding of data validation, API integration and custom business logic. If you need assistance configuring your Notion databases, customizing the JavaScript leakage formulas, integrating this workflow into your native ERP system or building similar supply chain automations, please contact WeblineIndia. Our team of technical integration experts can help you design, scale and maintain tailored automation workflows for your entire business.

Get weather alerts on your mobile phone via push, SMS or voice call. This flow gets weather information every morning and sends out an alert to your SIGNL4 on-call team. For example you can send out weather alerts in case of freezing temperatures, snow, rain, hail storms, hot weather, etc. The flow also supports automatic alert resolution. So, for example if the temperature goes up again the alert is closed automatically in the app. User cases: Dispatch snow removal teams Inform car dealers to protect the cars outside in case of hail storms Set sails if there are high winds And much more ... Can be adapted easily to other weather warnings, like rain, hail storm, etc.

Streamline client onboarding and project setup from hours to minutes with AI-driven automation. This intelligent workflow eliminates manual coordination, builds proposals, creates projects in Asana, welcomes clients on Slack, and logs everything — ensuring 90% faster onboarding and zero dropped steps. What This Workflow Does Transforms your client onboarding from scattered tools and emails into one seamless automation: 📝 Capture Client Details – Jotform intake form collects client, company, and project information. 🧠 AI-Powered Analysis – LangChain AI Agent analyzes the project scope, estimates effort, and recommends team composition. 📄 Generate Proposal – Automatically builds a professional HTML proposal summarizing goals, timeline, and estimated hours. 🗂️ Create Asana Project – Generates a new project with all key details, milestones, and assigned team members. 💬 Slack Collaboration – Creates a dedicated Slack channel, sends welcome messages, and introduces the project team. 📧 Welcome Email – Sends a personalized onboarding email to the client with project summary and next steps. 💼 CRM Sync – Creates or updates a HubSpot contact with complete project and client information. 📊 Audit Logging – Logs all onboarding activity to Google Sheets for centralized record-keeping. Key Features 🤖 AI Proposal Generation – Uses LangChain AI to generate smart project summaries and resource plans. ⚙️ End-to-End Automation – From form submission to project creation, communication, and CRM logging. 💬 Smart Slack Setup – Automatic channel creation and messaging for internal coordination. 📧 Personalized Client Emails – Beautifully formatted, professional onboarding emails. 🗂️ Asana Integration – Project creation with dynamic task templates and priorities. 📊 Google Sheets Logging – Instant audit trail for every client submission and generated proposal. 💼 CRM Integration – Automatically syncs client data with HubSpot for sales and account tracking. Perfect For 🚀 Agencies & Service Providers – Automate client onboarding, proposal creation, and task setup. 🏢 Consultancies – Quickly turn client requests into structured projects with assigned resources. 💻 Freelancers & Creators – Impress clients with AI-built proposals and instant communication. 📈 Growing Teams – Scale onboarding without extra admin or coordination time. 🧠 Operations Teams – Ensure consistency and transparency across all onboarding activities. What You’ll Need Required Integrations 🧾 Jotform – Client intake form (project details, budget, company info). Create your form for free on Jotform using this link 🤖 AI Agent – For analyzing project scope and building proposals. 🗂️ Asana – Project creation and task assignment. 💬 Slack – For automated client channel creation and internal communication. 📧 Gmail – For onboarding and proposal emails. 💼 HubSpot – CRM contact creation and project linkage. 📊 Google Sheets – For logging all onboarding and AI results. Optional Enhancements 📄 PDF Generation (PDF Munk) – Convert AI-generated proposals into downloadable PDFs. 💬 Slack Interactive Approvals – Add buttons for internal review before client communication. 📈 Performance Dashboard – Connect Google Sheets data to Looker Studio for tracking onboarding times. 🌍 Multilingual Support – Add translation nodes for international clients. 🔐 File Attachments – Send proposal PDFs or onboarding kits automatically via Gmail. Quick Start 1️⃣ Import Template – Copy and import the JSON file into your n8n workspace. 2️⃣ Set Up Jotform – Create a form with fields for client name, company, project name, budget, and requirements. 3️⃣ Add Credentials – Connect Jotform, AI Agent, Asana, Slack, Gmail, HubSpot, and Google Sheets. 4️⃣ Configure Sheet ID – Replace YOUR_SHEET_ID in the Google Sheets node. 5️⃣ Customize Proposal HTML – Edit AI prompt and branding to reflect your company’s style. 6️⃣ Test Workflow – Submit a test form and verify Slack, Asana, Gmail, and Sheets outputs. 7️⃣ Deploy – Activate workflow and share the Jotform link with your sales or operations team. Customization Options 1️⃣ Proposal Branding – Customize proposal HTML with logos, brand colors, and formatting. 2️⃣ AI Prompt Tuning – Refine the LangChain AI prompt to match your tone or project style. 3️⃣ Task Templates – Adjust task names, assignees, and due dates in the Asana creation node. 4️⃣ Slack Messaging – Update welcome message formatting and team introduction details. 5️⃣ CRM Fields – Map additional HubSpot properties for better data tracking. 6️⃣ Sheet Logging – Add more columns for tracking team recommendations or proposal scores. Expected Results ⚡ 90% Faster Onboarding – Reduce manual setup from hours to minutes. 🤖 AI Precision – Intelligent proposals and team allocations that impress clients instantly. 📈 Zero Missed Steps – Every project automatically created, communicated, and logged. 💬 Seamless Collaboration – Slack, Gmail, and Asana in perfect sync. 🗂️ Complete Transparency – Every onboarding step logged for accountability and improvement. 🏆 Use Cases 🧑‍💼 Marketing & Creative Agencies – Automate creative project scoping and proposal creation. 💻 Software Development Teams – Rapidly assess client tech requirements and allocate developers. 🧾 Consulting Firms – Build data-backed, AI-enhanced proposals for client engagements. 🏢 Corporate PMOs – Standardize project setup and approvals across multiple departments. Pro Tips 💡 Refine AI Prompt – Include examples of past projects to improve proposal quality. 💬 Add Slack Approvals – Insert “manager approval” logic before sending proposals. 📄 Attach PDFs – Use PDF Munk for branded, downloadable proposals. 📊 Track Conversion – Link HubSpot deal stage changes based on Asana progress. 📅 Monitor Efficiency – Use Sheet timestamps to calculate average onboarding time. Learning Resources This workflow demonstrates: AI integration using Agents Multi-app orchestration and data syncing Advanced HTML and email template customization Real-world Asana and Slack API usage CRM syncing and Google Sheets logging Modular, scalable n8n workflow design Workflow Structure Visualization 📝 Jotform Submission ↓ 🧠 AI Project Analysis (Agent) ↓ 📄 Proposal Generation (HTML) ↓ 🗂️ Asana Project Creation ↓ 💬 Slack Channel Setup & Message ↓ 📧 Gmail Welcome Email ↓ 💼 HubSpot Contact Creation ↓ 📊 Google Sheets Log Ready to Revolutionize Client Onboarding? Import this template today and let AI handle the heavy lifting. Your team saves hours, your clients get instant engagement — and your entire process runs flawlessly. ✨