Workflow Templates

Who is this for? This workflow is designed for HR professionals, employer branding teams, talent acquisition strategists, market researchers, and business intelligence analysts who want to monitor, understand, and act upon employee sentiment and company perception on Glassdoor. It's ideal for organizations that value real-time feedback, are tracking employer brand perception, or need summarized insights for leadership reporting without sifting through thousands of raw reviews. What problem is this workflow solving? Manually reviewing and analyzing Glassdoor reviews is tedious, subjective, and not scalable especially for larger companies or those with many subsidiaries. This workflow: Automates review collection by making a Glassdoor company request via the Bright Data Web Scrapper API. Uses Google Gemini to summarize the content. Sends an actionable summary to HR dashboards, leadership teams, or alert systems via the Webhook notification. What this workflow does Makes an HTTP Request to Glassdoor via the Bright Data Web Scrapper API. Polls the BrightData Glassdoor for the completion of the request. Downloads the Glassdoor response when a new snapshot is ready. Sends the prompt to Google Gemini for summarization. Delivers the summarized insights (strengths, weaknesses, sentiment, patterns) to a configured webhook or dashboard endpoint. 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). A webhook or endpoint to receive the summary (e.g., Slack, Notion, or custom HR dashboard). How to customize this workflow to your needs Change Summary Focus by updating the Summarization of Glassdoor Response node Summarization methods and prompts to extract specific insights: Cultural feedback Leadership issues Compensation comments Exit motivation Update the HTTP Request to Glassdoor node with a specific Glassdoor Company information that you are looking for. Format the output to produce a customized summary to Markdown or HTML for rich delivery. Integrate with HR Systems BambooHR, Workday, SAP SuccessFactors via API. Google Sheets or Airtable

Who is this for? This workflow is perfect for HR teams, recruiters, and hiring platforms that need to automate the extraction of key candidate details—like name, email, skills, and education—from resume files submitted in various formats. What problem does this solve? Manually reviewing and extracting structured data from resumes is time-consuming and error-prone. This automation eliminates that bottleneck, standardizing candidate data for seamless integration into CRMs, applicant tracking systems, or Google Sheets. What this workflow does This n8n template listens for uploaded resume files, detects their format (PDF, DOC, TXT, CSV, etc.), and automatically extracts the raw text using n8n’s built-in file extraction tools. The extracted text is then parsed using an OpenAI-powered agent that returns structured fields such as: Full Name Email Address Skill Keywords Education Details Optionally, you can push the structured output to Google Sheets (node included, currently disabled). Setup Clone this workflow into your n8n instance. Enable the When chat message received trigger if using n8n chat. Provide your OpenAI credentials and enable the LangChain Agent node. (Optional) Connect Google Sheets by authenticating with your Google account and filling in your target document and sheet. Watch the setup and demo video here: 🎥 https://youtu.be/2SUPiNmLWdA How to customize Modify the OpenAI system message to extract different fields (e.g., phone number, LinkedIn). Replace the Google Sheets node with a webhook to push results to your ATS. Add filters to limit accepted file types or max file size. > ⚠️ This template is designed to be secure. It uses credentials stored in the n8n credential manager—no hardcoded secrets required.

Mistral OCR is a super convenient way to parse and extract data from multi-page PDFs or single images using AI. What makes it special and differs it from the competition is that Mistral OCR also performs document page splitting and markdown conversion. This helps reduce dependencies required for document parsing workflows where tools like StirlingPDF. Read the official documentation on Mistral OCR API here: https://docs.mistral.ai/capabilities/document/#tag/ocr/operation/ocr_v1_ocr_post How it works To access Mistral-OCR, you'll need to use Mistral Cloud API via the HTTP request node Mistral OCR can only accept 2 file types: PDF and Image. Here, we use 2 different request to the Mistral-OCR API to parse a bank statement PDF and an screenshot of a bank statement to extract the tables. Next, we explore a more secure method of uploading documents to the Mistral OCR API by using Mistral's cloud storage. In example 2, we first store a copy of our documents to Mistral cloud and then generate a signed URL to retreive the file before sending it to Mistral OCR. This ensures the file is not accessible publicly and protects it from unauthorised access. Finally, another way to use Mistral-OCR is via document understanding. This allows you to ask questions about the document rather than extract contents from it. In example 3, I demonstrate this use-case asking Mistral-small to tell me how many deposits are shown in the bank statement. How to use Ensure your documents are either publicly accessible for Mistral-OCR or upload them to Mistral Cloud. Alternatively, signed urls from AWS S3 or Cloudflare R2 should also work. Requirements Mistral Cloud account and API Key. You'll also need credit on your account to use Mistral-OCR. Customising the workflow Mistral-OCR also works for images such as charts and diagrams so try using it on Financial Reports. Mistral-OCR is even cheaper with batching enabled. This returns your results within 24hrs but is half the price per page.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. **This n8n template automates candidate outreach, call transcription, and structured feedback capture for HR teams and recruiters. It triggers on a new candidate row added in a Google Sheet, initiates a call using Vapi.ai, processes the transcript using Google Gemini, extracts key information like CTC, experience, and notice period, and then updates the same Google Sheet with parsed insights. This is ideal for recruiters or HR teams conducting high-volume candidate outreach and wanting to scale initial data collection using automated voice bots and AI transcription analysis.** How it works Trigger: Listens for new rows added to a Google Sheet (e.g., a new candidate lead). Call Initiation: Uses Vapi.ai to make a phone call to the candidate using an assistant bot. Transcript Retrieval: After the call, fetches the conversation transcript from the Vapi API. AI Transcript Analysis: Google Gemini parses the transcript and extracts structured fields like: Work experience Current & expected CTC Notice period & negotiability Work preferences and location Data Mapping: Extracted insights are mapped to structured JSON fields. Google Sheet Update: The same row in the source Sheet is updated with the collected information. Use Cases Pre-screening calls for job applicants Collecting missing candidate information asynchronously Replacing manual HR data entry with AI-powered automation Smart CRM updates from voice interactions Requirements Before you run this workflow, ensure the following: ✅ Google account with access to Google Sheets API ✅ Vapi.ai account with: Assistant ID Phone number ID Active API key ✅ Google Gemini API (via PaLM) enabled ✅ n8n version 1.40.0 or later with relevant credentials configured How to use Import the workflow into n8n. Set up your credentials for: Google Sheets Trigger Google Sheets Vapi.ai (add Bearer token) Google Gemini Replace the placeholder values in: Assistant ID Phone number ID Google Sheet ID and tab Start the workflow and add a row to the Google Sheet. Wait for the automated call and let the AI extract and populate the data. Customising this workflow Replace Google Gemini with OpenAI or Claude if preferred. Add sentiment analysis on the transcript using an LLM. Modify the Sheet column structure to add additional fields. Add a filter node to skip candidates with incomplete phone numbers. Use a Webhook trigger instead of Google Sheets to integrate with job portals or ATS.

This template is for Self-Hosted N8N Instances only. This n8n demonstrates how to build a simple SQLite MCP server to perform local database operations as well as use it for Business Intelligence. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite How it works A MCP server trigger is used and connected to 5 tools: 2 Code Node and 3 Custom Workflow. The 2 Code Node tools use the SQLLite3 library and are simple read-only queries and as such, the Code Node tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our Code nodes to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This SQLite MCP server allows any compatible MCP client to manage a SQLite database by supporting select, create and update operations. You will need to have a SQLite database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please create a table to store business insights and add the following..." "what business insights do we have on current retail trends?" "Who has contributed the most business insights in the past week?" Requirements SQLite for database. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What is DIGIPIN? DIGIPIN (Digital Pincode) is a 10-character alphanumeric code introduced by India Post. It maps any 3x3 meter square in India to a unique digital address. This helps precisely locate homes, shops, or landmarks, especially in areas where physical addresses are inconsistent or missing. What this workflow does This workflow creates a fully offline DIGIPIN microservice using only JavaScript - no external APIs are used. You get two HTTP endpoints: GET /generate-digipin?lat={latitude}&lon={longitude} → returns a DIGIPIN GET /decode-digipin?digipin={code} → returns the latitude and longitude You can plug this into any system to: Convert GPS coordinates to a DIGIPIN Convert a DIGIPIN back to coordinates How it works An HTTP Webhook node receives the request A JS Function node either encodes or decodes based on input The result is returned as a JSON response All the logic is handled inside the workflow - no API keys, no external calls. Why use this Fast and lightweight Easily extendable: you can connect this to forms, CRMs, apps, or spreadsheets Ideal for field agents, address validation, logistics, or rural operations

How it works Fetch all workflows from your n8n instance. Filter workflows that contain nodes with a modelId setting. Extract the node names, model IDs, model names, workflow names, and workflow URLs. Save the extracted information into a connected Google Sheet. Set up steps Connect your n8n API credentials. Connect your Google Sheets account. Replace "Your n8n domain" with your actual domain URL. Use this Google Sheet template to create a new sheet for results. Setup typically takes 5 minutes. Be cautious: if you have over 100 workflows, performance may be impacted. Notes Sticky notes inside the workflow provide extra guidance. This workflow clears old sheet data before writing new results. Make sure your n8n instance allows API access. Result Example Update: It didn't detect the AI model in tool originally. Now it's fixed! Update 20250429: Support 1.91.0 with open node directly! Optimize the url with node id.

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs Add custom instructions to your Agent by changing the prompts in it. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. Describe your data properly in the Q&A tool Exchange the Simple Vector Store nodes with your own vector store tools ready for production. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.

How it works The workflow loads a list of test cases from a Google Sheet (previous results stored from an LLM) For each test case, we execute a call to an LLM judge in parallel (using HTTP Request + Webhook nodes) The judge uses the Input, Output, and Reference Answer fields from the spreadsheet to mark each LLM response as Pass/Fail The results are logged into a separate sheet in the same Sheets file. Set up steps: Add your credentials for Google Sheets and OpenRouter (or replace the OpenRouter node with your favourite chat model). Make a copy of the example Sheet to populate it with you own test data. Run the workflow with the Execute Workflow button next to the Manual Trigger node.

Who’s it for This template is designed for bakeries, event planners, and e-commerce platforms that want to automatically generate custom cake designs. It’s also ideal for marketers or digital creators who need personalized celebratory visuals for social media or email campaigns. How it works This workflow converts simple user input (e.g., “Sarah’s Birthday”) into a creative cake design: Webhook: Captures user input from the Bolt frontend form. OpenAI GPT: Generates a detailed and creative cake design prompt. Replicate Flux Schnell: Produces a unique cake image using the AI-generated prompt. HTTP Response: Sends the final cake image back to the frontend. How to set up Import this template into n8n. Add your OpenAI API Key under n8n Credentials for the OpenAI Chat Model node. Add your Replicate API Token as an HTTP Header Auth credential (do not hardcode it). Update the Webhook node URL in the Bolt frontend form to send a POST request to n8n. (Optional) Customize the OpenAI prompt in the Prompt Generator node to adjust cake style, colors, or decorations. Requirements n8n account (cloud or self-hosted). OpenAI API Key** for prompt generation. Replicate API Token** for AI image generation. A Bolt frontend or any form that can call the webhook endpoint. How to customize the workflow Replace "cake" with any product type (e.g., mugs, greeting cards, or T-shirts). Add a database node (Google Sheets or Supabase) to log user requests and images. Implement input moderation by adding an OpenAI moderation node before the prompt generation. Frontend

Yelp Business Scraper by URL via Bright Data API with Google Sheets Storage Overview This n8n workflow automates the process of scraping comprehensive business information from Yelp using individual business URLs. It integrates with Bright Data for professional web scraping and Google Sheets for centralized data storage, providing detailed business intelligence for market research, competitor analysis, and lead generation. Workflow Components 1. 📥 Form Trigger Type**: Form Trigger Purpose**: Initiates the workflow with user-submitted Yelp business URL Input Fields**: URL (Yelp business page URL) Function**: Captures target business URL to start the scraping process 2. 🔍 Trigger Bright Data Scrape Type**: HTTP Request (POST) Purpose**: Sends scraping request to Bright Data API for Yelp business data Endpoint**: https://api.brightdata.com/datasets/v3/trigger Parameters**: Dataset ID: gd_lgugwl0519h1p14rwk Include errors: true Limit multiple results: 5 Limit per input: 20 Function**: Initiates comprehensive business data extraction from Yelp 3. 📡 Monitor Snapshot Status Type**: HTTP Request (GET) Purpose**: Monitors the progress of the Yelp scraping job Endpoint**: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function**: Checks if the business data scraping is complete 4. ⏳ Wait 30 Sec for Snapshot Type**: Wait Node Purpose**: Implements intelligent polling mechanism Duration**: 30 seconds Function**: Pauses workflow before rechecking scraping status to optimize API usage 5. 🔁 Retry Until Ready Type**: IF Condition Purpose**: Evaluates scraping completion status Condition**: status === "ready" Logic**: True: Proceeds to data retrieval False: Loops back to status monitoring with wait 6. 📥 Fetch Scraped Business Data Type**: HTTP Request (GET) Purpose**: Retrieves the final scraped business information Endpoint**: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format**: JSON Function**: Downloads completed Yelp business data with comprehensive details 7. 📊 Store to Google Sheet Type**: Google Sheets Node Purpose**: Stores scraped business data for analysis and storage Operation**: Append rows Target**: "Yelp scraper data by URL" sheet Data Mapping**: Business Name, Overall Rating, Reviews Count Business URL, Images/Videos URLs Additional business metadata fields Workflow Flow Form Input → Trigger Scrape → Monitor Status → Wait 30s → Check Ready ↑ ↓ └─── Loop ─────┘ ↓ Fetch Data → Store to Sheet Configuration Requirements API Keys & Credentials Bright Data API Key**: Required for Yelp business scraping Google Sheets OAuth2**: For data storage and export access n8n Form Webhook**: For user input collection Setup Parameters Google Sheet ID**: Target spreadsheet identifier Dataset ID**: gd_lgugwl0519h1p14rwk (Yelp business scraper) Form Webhook ID**: User input form identifier Google Sheets Credential ID**: OAuth2 authentication Key Features Comprehensive Business Data Extraction Complete business profile information Customer ratings and review counts Contact details and business hours Photo and video content URLs Location and category information Intelligent Status Monitoring Real-time scraping progress tracking Automatic retry mechanisms with 30-second intervals Status validation before data retrieval Error handling and timeout management Centralized Data Storage Automatic Google Sheets export Organized business data format Historical scraping records Easy sharing and collaboration URL-Based Processing Direct Yelp business URL input Single business deep-dive analysis Flexible input through web form Real-time workflow triggering Use Cases Market Research Competitor business analysis Local market intelligence gathering Industry benchmark establishment Service offering comparison Lead Generation Business contact information extraction Potential client identification Market opportunity assessment Sales prospect development Business Intelligence Customer sentiment analysis through ratings Competitor performance monitoring Market positioning research Brand reputation tracking Location Analysis Geographic business distribution Local competition assessment Market saturation evaluation Expansion opportunity identification Data Output Fields | Field | Description | Example | |-------|-------------|---------| | Name | Business name | "Joe's Pizza Restaurant" | | Overall Rating | Average customer rating | "4.5" | | Reviews Count | Total number of reviews | "247" | | URL | Original Yelp business URL | "https://www.yelp.com/biz/joes-pizza..." | | Images/Videos URLs | Media content links | "https://s3-media1.fl.yelpcdn.com/..." | Technical Notes Polling Interval**: 30-second status checks for optimal API usage Result Limiting**: Maximum 20 businesses per input, 5 multiple results Data Format**: JSON with structured field mapping Error Handling**: Comprehensive error tracking in all API requests Retry Logic**: Automatic status rechecking until completion Form Input**: Single URL field with validation Storage Format**: Structured Google Sheets with predefined columns Setup Instructions Step 1: Import Workflow Copy the JSON workflow configuration Import into n8n: Workflows → Import from JSON Paste configuration and save Step 2: Configure Bright Data Set up credentials: Navigate to Credentials → Add Bright Data API Enter your Bright Data API key Test connection Update API key references: Replace BRIGHT_DATA_API_KEY in all HTTP request nodes Verify dataset access for gd_lgugwl0519h1p14rwk Step 3: Configure Google Sheets Create target spreadsheet: Create new Google Sheet named "Yelp Business Data" or similar Copy the Sheet ID from URL Set up OAuth2 credentials: Add Google Sheets OAuth2 credential in n8n Complete authentication process Update workflow references: Replace YOUR_GOOGLE_SHEET_ID with actual Sheet ID Update YOUR_GOOGLE_SHEETS_CREDENTIAL_ID with credential reference Step 4: Test and Activate Test with sample URL: Use a known Yelp business URL Monitor execution progress Verify data appears in Google Sheet Activate workflow: Toggle workflow to "Active" Share form URL with users Sample Business Data The workflow captures comprehensive business information including: Basic Information**: Name, category, location Performance Metrics**: Ratings, review counts, popularity Contact Details**: Phone, website, address Visual Content**: Photos, videos, gallery URLs Operational Data**: Hours, services, amenities Customer Feedback**: Review summaries, sentiment indicators Advanced Configuration Batch Processing Modify the input to accept multiple URLs: [ {"url": "https://www.yelp.com/biz/business-1"}, {"url": "https://www.yelp.com/biz/business-2"}, {"url": "https://www.yelp.com/biz/business-3"} ] Enhanced Data Fields Add more extraction fields by updating the dataset configuration: Business hours and schedule Menu items and pricing Customer photos and reviews Special offers and promotions Notification Integration Add alert mechanisms: Email notifications for completed scrapes Slack messages for team updates Webhook triggers for external systems Error Handling Common Issues Invalid URL**: Ensure URL is a valid Yelp business page Rate Limiting**: Bright Data API usage limits exceeded Authentication**: Google Sheets or Bright Data credential issues Data Format**: Unexpected response structure from Yelp Troubleshooting Steps Verify URLs: Ensure Yelp business URLs are correctly formatted Check Credentials: Validate all API keys and OAuth tokens Monitor Logs: Review n8n execution logs for detailed errors Test Connectivity: Verify network access to all external services Performance Specifications Processing Time**: 2-5 minutes per business URL Data Accuracy**: 95%+ for publicly available business information Success Rate**: 90%+ for valid Yelp business URLs Concurrent Processing**: Depends on Bright Data plan limits Storage Capacity**: Unlimited (Google Sheets based) **For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

Explorium Prospects Search Chatbot Template Download the following json file and import it to a new n8n workflow: mcp\_to\_prospects\_to\_csv.json Overview This n8n workflow creates a chatbot that understands natural language requests for finding business prospects and automatically: Interprets your query using AI (Claude Sonnet 3.7) Converts it to proper Explorium API filters Validates the API request structure Fetches prospect data from Explorium Exports results as a downloadable CSV file Perfect for sales teams, recruiters, and business development professionals who need to quickly find and export targeted prospect lists without learning complex API syntax. Key Features Natural Language Interface**: Simply describe who you're looking for in plain English Smart Query Translation**: AI converts your request to valid API parameters Built-in Validation**: Ensures API calls meet Explorium's requirements Error Recovery**: Automatically retries with corrections if validation fails Pagination Support**: Handles large result sets automatically CSV Export**: Clean, formatted output ready for CRM import Conversation Memory**: Maintains context for follow-up queries Example Queries The chatbot understands queries like: "Find marketing directors at SaaS companies in New York with 50-200 employees" "Get me CTOs from fintech startups in California" "Show me sales managers at healthcare companies with revenue over $10M" "Find engineers at Microsoft with 3-5 years experience" "Get customer service leads from e-commerce companies in Europe" Prerequisites Before setting up this workflow, ensure you have: n8n instance with chat interface enabled Anthropic API key for Claude Explorium API credentials (Bearer token) - Get explorium api key Basic understanding of n8n chat workflows Supported Filters The chatbot can search using these criteria: Company Filters Size**: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+ employees Revenue**: Ranges from $0-500K up to $10T+ Age**: 0-3, 3-6, 6-10, 10-20, 20+ years Location**: Countries, regions, cities Industry**: Google categories, NAICS codes, LinkedIn categories Name**: Specific company names Prospect Filters Job Level**: CXO, VP, Director, Manager, Senior, Entry, etc. Department**: Sales, Marketing, Engineering, Finance, HR, etc. Experience**: Total months and current role duration Location**: Country and region codes Contact Info**: Filter by email/phone availability Installation & Setup Step 1: Import the Workflow Copy the workflow JSON from the template In n8n: Workflows → Add Workflow → Import from File Paste the JSON and click Import Step 2: Configure Anthropic Credentials Click on the Anthropic Chat Model1 node Under Credentials, click Create New Add your Anthropic API key Name: "Anthropic API" Save credentials Step 3: Configure Explorium Credentials You'll need to set up Explorium credentials in two places: For MCP Client: Click on the MCP Client node Under Credentials, create new Header Auth Add your authentication header (usually Authorization: Bearer YOUR_TOKEN) Save credentials For API Calls: Click on the Prospects API Call node Use the same Header Auth credentials created above Verify the API endpoint is correct Step 4: Activate the Workflow Save the workflow Click the Active toggle to enable it The chat interface will now be available Step 5: Access the Chat Interface Click on the When chat message received node Copy the webhook URL Access this URL in your browser to start chatting How It Works Workflow Architecture Chat Trigger: Receives natural language queries from users Memory Buffer: Maintains conversation context AI Agent: Interprets queries and generates API parameters Validation: Checks API structure against Explorium requirements API Call: Fetches prospect data with pagination Data Processing: Formats results for CSV export File Conversion: Creates downloadable CSV file Processing Flow User Query → AI Interpretation → Validation → API Call → CSV Export ↑ ↓ └──── Error Correction Loop ←──────┘ Validation Rules The workflow validates: Filter keys are allowed by Explorium API Values match expected formats (e.g., valid country codes) Range filters have proper gte/lte values No duplicate values in arrays Required structure is maintained Usage Guide Basic Conversation Flow Start with your query: "Find me VPs of Sales at software companies in the US" Bot processes and responds: Generates API filters Validates the structure Fetches data Returns CSV download link Refine if needed: "Can you also include directors and filter for companies with 100+ employees?" Query Tips Be specific**: Include job titles, departments, company details Use standard terms**: "CTO" instead of "Chief Technology Officer" Specify locations**: Use country names or standard codes Include size/revenue**: Helps narrow results effectively Advanced Queries Combine multiple criteria: "Find engineering managers and senior engineers at B2B SaaS companies in New York and California with 50-500 employees and revenue over $5M who have been in their role for at least 1 year" Output Format The CSV file includes: Prospect ID Name (first, last, full) Location (country, region, city) LinkedIn profile Experience summary Skills and interests Company details Job information Business ID Troubleshooting Common Issues "Validation failed" errors Check that your query uses supported filter values Ensure location names are spelled correctly Verify company sizes/revenues match allowed ranges No results returned Broaden your search criteria Check if the company exists in Explorium's database Verify filter combinations aren't too restrictive Chat not responding Ensure workflow is activated Check all credentials are properly configured Verify webhook URL is accessible Large result sets timing out Try adding more specific filters Limit results by location or company size Use the size parameter (max 10,000) Error Messages The bot provides clear feedback: Invalid filters**: Shows which filters aren't supported Value errors**: Lists correct options for each field API failures**: Explains connection or authentication issues Performance Optimization Best Practices Start broad, then narrow: Begin with basic criteria and add filters Use business IDs: When targeting specific companies Limit by contact info: Add has_email: true for actionable leads Batch by location: Process regions separately for large searches API Limits Maximum 10,000 results per search Pagination handles up to 100 records per page Rate limits apply based on your Explorium subscription Customization Options Modify AI Behavior Edit the AI Agent system message to: Change response format Add custom filters Adjust interpretation logic Include additional instructions Extend Functionality Add nodes to: Send results via email Import directly to CRM Schedule recurring searches Create custom reports Integration Ideas Connect to Slack for team queries Add to CRM workflows Create lead scoring systems Build automated outreach campaigns Security Considerations API credentials are stored securely in n8n Chat sessions are isolated No prospect data is stored permanently CSV files are generated on-demand Support Resources For issues with: n8n platform**: Check n8n documentation Explorium API**: Contact Explorium support Anthropic/Claude**: Refer to Anthropic docs Workflow logic**: Review node configurations