Workflow Templates

Upload a construction photo via web form → get a detailed cost estimate with work breakdown, resource costs, and professional HTML report. Powered by GPT-4 Vision and the open-source DDC CWICR database (55,000+ work items). Who's it for Site managers** who need quick estimates from mobile photos Renovation contractors** evaluating project scope from initial site visit Real estate inspectors** estimating repair costs Construction consultants** providing rapid ballpark figures DIY enthusiasts** planning home improvement budgets What it does Collects photo + region/language via n8n Form Analyzes photo with GPT-4 Vision (room type, elements, dimensions) Decomposes visible elements into construction work items Searches DDC CWICR vector database for matching rates Generates professional HTML report with cost breakdown Supports 9 regions: 🇩🇪 Berlin · 🇬🇧 Toronto · 🇷🇺 St. Petersburg · 🇪🇸 Barcelona · 🇫🇷 Paris · 🇧🇷 São Paulo · 🇨🇳 Shanghai · 🇦🇪 Dubai · 🇮🇳 Mumbai How it works ┌──────────────┐ ┌───────────────┐ ┌───────────────┐ ┌──────────────┐ │ Web Form │ → │ STAGE 1 │ → │ STAGE 4 │ → │ Loop Works │ │ Photo+Lang │ │ GPT-4 Vision │ │ Decompose │ │ per item │ └──────────────┘ └───────────────┘ └───────────────┘ └──────────────┘ ↓ ↓ ↓ ┌─────────────────────────────────────────────────────┐ │ Identify room, elements, fixtures, dimensions │ │ → Break down into 15-40 construction work items │ └─────────────────────────────────────────────────────┘ ↓ ┌──────────────┐ ┌───────────────┐ ┌───────────────┐ ┌──────────────┐ │ HTML Report │ ← │ STAGE 7.5 │ ← │ STAGE 5 │ ← │ Qdrant │ │ Response │ │ Aggregate │ │ Parse+Score │ │ Vector DB │ └──────────────┘ └───────────────┘ └───────────────┘ └──────────────┘ Pipeline stages: | Stage | Node | Description | |-------|------|-------------| | 1 | GPT-4 Vision | Analyzes photo: room type, elements, materials, dimensions | | 4 | GPT-4 Decompose | Breaks elements into work items with quantities | | 5 | Vector Search + Score | Finds matching rates in DDC CWICR, quality scoring | | 7.5 | Aggregate & Validate | Sums costs, groups by phase, validates results | | 9 | HTML Report | Generates professional estimate document | Prerequisites | Component | Requirement | |-----------|-------------| | n8n | v1.30+ with Form Trigger support | | OpenAI API | GPT-4 Vision + Embeddings access | | Qdrant | Vector DB with DDC CWICR collections | | DDC CWICR Data | github.com/datadrivenconstruction/DDC-CWICR | Setup 1. n8n Credentials (Settings → Credentials) OpenAI API** — required (GPT-4 Vision + text-embedding-3-large) Qdrant API** — your Qdrant instance connection 2. Qdrant Collections Load DDC CWICR embeddings for your target regions: DE_BERLIN_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ENG_TORONTO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR RU_STPETERSBURG_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ES_BARCELONA_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR FR_PARIS_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR PT_SAOPAULO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ZH_SHANGHAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR AR_DUBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR HI_MUMBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR 3. Activate Workflow Import JSON into n8n Link OpenAI + Qdrant credentials to respective nodes Activate workflow Access form at: https://your-n8n/form/photo-estimate-pro-v3 Features | Feature | Description | |---------|-------------| | 📸 Photo Analysis | GPT-4 Vision identifies room type, elements, fixtures | | 📏 Dimension Estimation | Uses reference objects (doors, tiles) for sizing | | 🔧 Work Decomposition | Breaks down to 15-40 specific work items | | 🎯 Quality Scoring | Rates match quality (high/medium/low/not_found) | | 📊 Phase Grouping | PREPARATION → MAIN → FINISHING → MEP | | 💰 Cost Breakdown | Labor, materials, machines per item | | ✅ Validation | Warns if <50% rates found or missing demolition | | 🌍 9 Languages | Full localization + regional pricing | Form Fields | Field | Type | Options | |-------|------|---------| | 📷 Upload Photo | File | .jpg, .png, .webp | | 🌍 Region & Language | Dropdown | 9 regions with currencies | | 🏗️ Work Type | Dropdown | New / Renovation / Repair / Auto | | 📝 Description | Textarea | Optional context | Example Output Input: Bathroom photo (renovation) Region: 🇩🇪 German - Berlin (EUR €) Generated Work Items: PREPARATION (3 items) ├── Demolition of wall tiles — 12 m² — €180 ├── Demolition of floor tiles — 4.5 m² — €95 └── Disposal of construction waste — 0.8 m³ — €120 MAIN (8 items) ├── Floor waterproofing — 4.5 m² — €225 ├── Wall waterproofing wet zone — 8 m² — €280 ├── Floor screed — 4.5 m² — €135 ├── Wall tiling — 22 m² — €880 ├── Floor tiling — 4.5 m² — €225 ├── Toilet installation — 1 pcs — €320 ├── Sink installation — 1 pcs — €185 └── Shower cabin installation — 1 pcs — €450 FINISHING (3 items) ├── Ceiling painting — 4.5 m² — €68 ├── Grouting — 26.5 m² — €133 └── Silicone sealing — 8 m — €48 MEP (4 items) ├── Socket installation — 2 pcs — €90 ├── Light point installation — 2 pcs — €120 ├── Mixer/faucet installation — 2 pcs — €160 └── Ventilation installation — 1 pcs — €85 ───────────────────────────────────── TOTAL: €3,799.00 Labor: €1,520 · Materials: €1,900 · Machines: €379 Quality: 78% high match · 18 work items Quality Scoring System | Score | Level | Meaning | |-------|-------|---------| | 60-100 | 🟢 High | Exact match with resources | | 40-59 | 🟡 Medium | Good match, minor differences | | 20-39 | 🟠 Low | Partial match, review needed | | 0-19 | 🔴 Not Found | No suitable rate found | Scoring factors: Has price in database (+30) Has resources breakdown (+25) Unit matches expected (+20) Material keywords match (+15) Work type keywords match (+10) Vector similarity >0.5 (+10) Notes & Tips Best photo angles:** Capture full room, include reference objects (doors, sockets) Renovation mode:** AI automatically adds demolition works Validation warnings:** Check if <50% rates found — may need manual additions Rate accuracy:** Depends on DDC CWICR coverage for your region Extend:** Chain with PDF generation, email delivery, or CRM integration Categories AI · Data Extraction · Document Ops · Files & Storage Tags photo-analysis, gpt-4-vision, construction, cost-estimation, qdrant, vector-search, form-trigger, html-report, multilingual Author DataDrivenConstruction.io https://DataDrivenConstruction.io info@datadrivenconstruction.io Consulting & Training We help construction, engineering, and technology firms implement: AI-powered visual estimation systems CAD/BIM data processing pipelines Vector database integration for construction data Multilingual cost database solutions Contact us to test with your data or adapt to your project requirements. Resources DDC CWICR Database:** GitHub Qdrant Documentation:** qdrant.tech/documentation n8n Form Trigger:** docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger ⭐ Star us on GitHub! github.com/datadrivenconstruction/DDC-CWICR

How It Works This workflow automates complex data engineering operations by orchestrating multiple specialized AI agents to analyze datasets, calculate risk metrics, and route findings based on severity levels. Designed for data engineers, analytics teams, and business intelligence managers, it solves the challenge of processing diverse datasets through appropriate analytical frameworks while ensuring critical insights reach stakeholders immediately. The system receives data processing requests via webhook, deploys an orchestration agent that determines which specialized analysis agents to invoke (Anthropic Chat Model for general analysis, Risk Analysis Verification Agent, and Test Validation Agent), calculates risk scores, fetches relevant historical context, then routes results by severity. High-severity findings trigger immediate HTTP notifications to stakeholders, while all results are aggregated into comprehensive reports, formatted for clarity, and logged with appropriate priority markers before webhook response. Setup Steps Configure webhook trigger endpoint for data processing system integration Set up Anthropic API credentials for Orchestrating Orchestration Agent node Configure specialized agent tools Update Calculate Risk Score node with your risk scoring methodology Set up Fetch Historical Data node with data warehouse API credentials Configure severity threshold in Route by Severity node for alert triggering Connect HTTP Request nodes with stakeholder notification endpoints Prerequisites Active Anthropic and OpenAI API accounts, data processing system with webhook capability Use Cases ETL pipeline quality monitoring, data anomaly detection, dataset validation before production deployment Customization Modify orchestration agent logic for custom analysis pathways Benefits Accelerates data quality assessment by 70%, enables proactive issue detection before production impact

Instantly access live OKX Spot Market data directly in Telegram! This workflow integrates the OKX REST v5 API with Telegram and optional GPT-4.1-mini formatting, delivering real-time insights such as latest prices, order book depth, candlesticks, trades, and mark prices — all in clean, structured reports. 🔎 How It Works A Telegram Trigger node listens for incoming user commands. The User Authentication node validates the Telegram ID to allow only authorized users. The workflow creates a Session ID from chat.id to manage session memory. The OKX AI Agent orchestrates data retrieval via HTTP requests to OKX endpoints: Latest Price (/api/v5/market/ticker?instId=BTC-USDT) 24h Stats (/api/v5/market/ticker?instId=BTC-USDT) Order Book Depth (/api/v5/market/books?instId=BTC-USDT&sz=50) Best Bid/Ask (book ticker snapshot) Candlesticks / Klines (/api/v5/market/candles?instId=BTC-USDT&bar=15m) Average / Mark Price (/api/v5/market/mark-price?instType=SPOT&instId=BTC-USDT) Recent Trades (/api/v5/market/trades?instId=BTC-USDT&limit=100) Utility tools refine the data: Calculator → spreads, % change, normalized volumes. Think → reshapes raw JSON into clean text. Simple Memory → stores sessionId, symbol, and state for multi-turn interactions. A message splitter ensures Telegram output stays under 4000 characters. Final results are sent to Telegram in structured, human-readable format. ✅ What You Can Do with This Agent Get latest price and 24h stats for any Spot instrument. Retrieve order book depth with configurable size (up to 400 levels). View best bid/ask snapshots instantly. Fetch candlestick OHLCV data across intervals (1m → 1M). Monitor recent trades (up to 100). Check the mark price as a fair average reference. Receive clean, Telegram-ready reports (auto-split if too long). 🛠️ Setup Steps Create a Telegram Bot Use @BotFather to generate a bot token. Configure in n8n Import OKX AI Agent v1.02.json. Replace the placeholder in User Authentication node with your Telegram ID. Add Telegram API credentials (bot token). Add your OpenAI API key for GPT-4.1-mini. Add your OKX API key optional. Deploy and Test Activate the workflow in n8n. Send a query like BTC-USDT to your bot. Instantly get structured OKX Spot data back in Telegram. 📺 Setup Video Tutorial Watch the full setup guide on YouTube: ⚡ Unlock real-time OKX Spot Market insights directly in Telegram — no private API keys required! 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: Don Jayamaha – LinkedIn

This n8n template from Intuz provides a complete solution to automate the extraction of critical information from PDF documents like faxes, or any PDFs. It uses the power of Google Gemini's multimodal capabilities to read the document, identify key fields, and organize the data into a structured format, saving it directly to a Google Sheet. Who's this workflow for? Healthcare Administrators Medical Billing Teams Legal Assistants Data Entry Professionals Office Managers How it works 1. Upload via Web Form: The process starts when a user uploads a fax (as a PDF file) through a simple, secure web form generated by n8n. 2. AI Document Analysis: The PDF is sent directly to Google Gemini's advanced multimodal model, which reads the entire document—including text, tables, and form fields. It extracts all relevant information based on a detailed prompt. 3. AI Data Structuring: The raw extracted text is then passed to a second AI step. This step cleans the information and strictly structures it into a predictable JSON format (e.g., Patient ID, Name, DOB, etc.). 4. Save to Google Sheets: The final, structured data is automatically appended as a new, clean row in your designated Google Sheet, creating an organized and usable dataset from the unstructured fax. Key Requirements to Use This Template 1. n8n Instance & Required Nodes: An active n8n account (Cloud or self-hosted). This workflow uses the official n8n LangChain integration (@n8n/n8n-nodes-langchain). If you are using a self-hosted version of n8n, please ensure this package is installed. 2. Google Accounts: Google Drive Account: For temporarily storing the uploaded file. Google Gemini AI Account: A Google Cloud account with the Vertex AI API (for Gemini models) enabled and an associated API Key. Google Sheets Account: A pre-made Google Sheet with columns that match the data you want to extract. Customer Setup Guide: Here is a detailed, step-by-step guide to help you configure and run this workflow. 1. Before You Begin: Prerequisites Please ensure you have the following ready: The FAX-Content-Extraction.json file we provided. Active accounts for n8n, Google Drive, Google Cloud (for Gemini AI), and Google Sheets. A Google Sheet created with header columns that match the data you want to extract (e.g., Patient ID, Patient Name, Date of Birth, etc.). 2. Step-by-Step Configuration Step 1: Import the Workflow Open your n8n canvas. Click "Import from File" and select the FAX-Content-Extraction.json file. The workflow will appear on your canvas. Step 2: Set Up the Form Trigger The workflow starts with the "On form submission" node. Click on this node. In the settings panel, you will see a "Form URL". Copy this URL. This is the link to the web form where you will upload your fax files. Step 3: Configure the Google Drive Node Click on the "Upload file" (Google Drive) node. Credentials: Select your Google Drive account from the "Credentials" dropdown or click "Create New" to connect your account. Folder ID: In the "Folder ID" field, choose the specific Google Drive folder where you want the uploaded faxes to be saved. Step 4: Configure the Google Gemini AI Nodes (Very Important) This workflow uses AI in two places, and both need to be connected. First AI Call (PDF Reading): Click on the "Call Gemini 2.0 Flash with PDF Capabilities" (HTTP Request) node. Under "Authentication", make sure "Predefined Credential Type" is selected. For "Credential Type", choose "Google Palm API". In the "Credentials" dropdown, select your Google Gemini API key or click "Create New" to add it. Second AI Call (Data Structuring): Click on the "Google Gemini Chat Model" node (it's connected below the "Basic LLM Chain" node). In the "Credentials" dropdown, select the same Google Gemini API key you used before. Step 5: (Optional) Customize What Data is Extracted You have full control over what information the AI looks for. To change the extraction rules: Click on the "Define Prompt" node. You can edit the text in the "Value" field to tell the AI what to look for (e.g., "Extract only the patient's name and medication list"). To change the final output columns: Click on the "Basic LLM Chain" node. In the "Text" field, you can edit the JSON schema to add, remove, or rename the fields you want in your final output. The keys here MUST match the column headers in your Google Sheet. Step 6: Configure the Final Google Sheets Node Click on the "Append row in sheet" node. Credentials: Select your Google Sheets account from the "Credentials" dropdown. Document ID: Select your target spreadsheet from the "Document" dropdown list. Sheet Name: Select the specific sheet within that document. Columns: Ensure that the fields listed here match the columns in your sheet and the schema from the "Basic LLM Chain" node. 4. Running the Workflow Save and Activate: Click "Save" and then toggle the workflow to "Active". Open the Form: Open the Form URL you copied in Step 2 in a new browser tab. Upload a File: Upload a sample fax PDF and submit the form. Check Your Sheet: After a minute, a new row with the extracted data should appear in your Google Sheet. Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Worflow Automation Click here- Get Started

How it works Multi-modal AI Image Generator powered by Google's Nano Banana (Gemini 2.5 Flash Image) - the latest state-of-the-art image generation model Accepts text, images, voice messages, and PDFs via Telegram for maximum flexibility Uses OpenAI GPT models for conversation and image analysis, then Nano Banana for stunning image generation Features conversation memory for iterative image modifications ("make it darker", "change to blue") Processes different input types: analyzes uploaded images, transcribes voice messages, extracts PDF text All inputs are converted to optimized prompts specifically tuned for Nano Banana's capabilities Set up steps Create Telegram bot via @BotFather and get API token Set up Google Gemini API key from Google AI Studio for Nano Banana image generation (~$0.04/image) Configure OpenAI API key for GPT models (conversation, image analysis, voice transcription) Import workflow and configure all three API credentials in n8n Update bot tokens in HTTP request nodes for file downloads Test with text prompts, image uploads, voice messages, and PDF documents

This n8n template demonstrates how to fully automate the creation of UGC-style product videos using AI, starting from a simple Google Sheet. It transforms product data into AI-generated images, cinematic video scripts, and final videos, then uploads everything to Google Drive and updates your sheet automatically. 💡 Use cases Generate UGC ads at scale for e-commerce products Create TikTok / Reels content automatically Build content pipelines for agencies or creators Rapidly test different product angles, audiences, and messaging Automate creative production from structured data (Google Sheets) Good to know This workflow uses multiple AI services (image + video), so cost depends on usage: Image generation (DALL·E) Video generation (Sora) Video generation is asynchronous and may take several minutes per item Some AI models (like Sora) may be region-restricted or limited access Generated image URLs may expire, so storing them (as done here) is important How it works Reads product data from Google Sheets and selects rows marked "Pending". Creates a prompt and generates a product image. Analyzes the image and turns it into a video script. Sends the script to Sora and waits until the video is ready. Uploads the video to Google Drive and updates the sheet. Logs errors and marks the row as "Error". How to use Add products to Google Sheets with name, description, audience, and set status to "Pending". Run the workflow or let the schedule trigger process items automatically. The system generates image → script → video, uploads them, and updates your sheet. Requirements Requires OpenAI, Google Sheets, and Google Drive accounts. Requires an n8n instance with credentials configured. Customizing this workflow Replace the schedule trigger with a webhook or form for real-time use. Generate multiple videos per product. Send outputs to platforms like TikTok, Meta Ads, or CMS tools. Add voiceovers, captions, or permanent asset storage.

Overview This workflow enables GDPR-compliant document processing by detecting, masking, and securely handling personally identifiable information (PII) before AI analysis. It ensures that sensitive data is never exposed to AI systems by replacing it with tokens, while still allowing controlled re-injection of original values when permitted. The workflow also maintains full audit logs for compliance and traceability. How It Works Document Upload & Configuration Receives documents via webhook and initializes configuration such as document ID, thresholds, and database tables. Text Extraction Extracts raw text from uploaded documents for processing. Multi-Detector PII Detection Detects emails, phone numbers, ID numbers, and addresses using regex and AI-based detection. PII Aggregation & Conflict Resolution Merges detections, resolves overlaps, removes duplicates, and builds a unified PII map. Tokenization & Vault Storage Replaces sensitive data with secure tokens and stores original values in a database vault. Masking & Validation Generates masked text and verifies that all PII has been successfully removed before AI processing. AI Processing (Masked Data) Processes the document using AI while preserving tokens to prevent exposure of sensitive information. Re-Injection Controller Determines which fields are allowed to restore original PII based on permissions. Secure Retrieval & Restoration Retrieves original values from the vault and restores them only where permitted. Audit Logging Stores metadata, detected PII types, and re-injection events for compliance tracking. Error Handling & Alerts Blocks processing and triggers alerts if masking fails or compliance rules are violated. Setup Instructions Activate the webhook and upload a document (PDF or supported file) Configure AI credentials (Anthropic / OpenAI) Set database credentials for PII vault and audit logs Adjust detection thresholds and compliance settings if needed Execute the workflow and review outputs and logs Use Cases GDPR-compliant document processing pipelines Secure AI document analysis with PII protection Automated redaction and tokenization systems Financial, legal, or healthcare document processing Privacy-first AI workflows for sensitive data Requirements n8n (latest version recommended) Anthropic or OpenAI API credentials PostgreSQL (or compatible database) for vault and audit logs Input documents (PDF or text-based files)

Transform college admissions from an overwhelming manual process into an intelligent, efficient, and equitable system that analyzes essays, scores applicants holistically, and identifies top candidates—saving 40+ hours per week while improving decision quality. 🎯 What This Workflow Does Automates comprehensive application review with AI-powered analysis: 📝 Application Intake - Captures complete college applications via Jotform 📚 AI Essay Analysis - Deep analysis of personal statements and supplemental essays for: Writing quality, authenticity, and voice AI-generated content detection Specificity and research quality Red flags (plagiarism, inconsistencies, generic writing) 🎯 Holistic Review AI - Evaluates applicants across five dimensions: Academic strength (GPA, test scores, rigor) Extracurricular profile (leadership, depth, impact) Personal qualities (character, resilience, maturity) Institutional fit (values alignment, contribution potential) Diversity contribution (unique perspectives, experiences) 🚦 Smart Routing - Automatically categorizes and routes applications: Strong Admit (85-100): Slack alert → Director email → Interview invitation → Fast-track Committee Review (65-84): Detailed analysis → Committee discussion → Human decision Standard Review (<65): Acknowledgment → Human verification → Standard timeline 📊 Comprehensive Analytics - All applications logged with scores, recommendations, and outcomes ✨ Key Features AI Essay Analysis Engine Writing Quality Assessment**: Grammar, vocabulary, structure, narrative coherence Authenticity Detection**: Distinguishes genuine voice from AI-generated content (GPT detectors) Content Depth Evaluation**: Self-awareness, insight, maturity, storytelling ability Specificity Scoring**: Generic vs tailored "Why Us" essays with research depth Red Flag Identification**: Plagiarism indicators, privilege blindness, inconsistencies, template writing Thematic Analysis**: Core values, motivations, growth narratives, unique perspectives Holistic Review Scoring (0-100 Scale) Academic Strength (35%)**: GPA in context, test scores, course rigor, intellectual curiosity Extracurricular Profile (25%)**: Quality over quantity, leadership impact, commitment depth Personal Qualities (20%)**: Character, resilience, empathy, authenticity, self-awareness Institutional Fit (15%)**: Values alignment, demonstrated interest, contribution potential Diversity Contribution (5%)**: Unique perspectives, life experiences, background diversity Intelligent Candidate Classification Admit**: Top 15% - clear admit, exceptional across multiple dimensions Strong Maybe**: Top 15-30% - competitive, needs committee discussion Maybe**: Top 30-50% - solid but not standout, waitlist consideration Deny**: Below threshold - does not meet competitive standards (always human-verified) Automated Workflows Priority Candidates**: Immediate Slack alerts, director briefs, interview invitations Committee Cases**: Detailed analysis packets, discussion points, voting workflows Standard Processing**: Professional acknowledgments, timeline communications Interview Scheduling**: Automated invitations with candidate-specific questions 💼 Perfect For Selective Colleges & Universities**: 15-30% acceptance rates, holistic review processes Liberal Arts Colleges**: Emphasis on essays, personal qualities, institutional fit Large Public Universities**: Processing thousands of applications efficiently Graduate Programs**: MBA, law, medical school admissions Scholarship Committees**: Evaluating merit and need-based awards Honors Programs**: Identifying top candidates for selective programs Private High Schools**: Admissions teams with holistic processes 🎓 Admissions Impact Efficiency & Productivity 40-50 hours saved per week** on initial application review 70% faster** essay evaluation with AI pre-analysis 3x more applications** processed per reader Zero data entry** - all information auto-extracted Consistent evaluation** across thousands of applications Same-day turnaround** for top candidate identification Decision Quality Improvements Objective scoring** reduces unconscious bias Consistent criteria** applied to all applicants Essay authenticity checks** catch AI-written applications Holistic view** considers all dimensions equally Data-driven insights** inform committee discussions Fast-track top talent** before competitors Equity & Fairness Standardized evaluation** ensures fair treatment First-generation flagging** provides context Socioeconomic consideration** in holistic scoring Diverse perspectives valued** in diversity score Bias detection** in essay analysis Audit trail** for compliance and review Candidate Experience Instant acknowledgment** of application receipt Professional communication** at every stage Clear timelines** and expectations Interview invitations** for competitive candidates Respectful process** for all applicants regardless of outcome 🔧 What You'll Need Required Integrations Jotform** - Application intake forms Create your form for free on JotForm using this link OpenAI API** - GPT-4o for analysis (~$0.15-0.25 per application) Gmail/Outlook** - Applicant and staff communication (free) Google Sheets** - Application database and analytics (free) Optional Integrations Slack** - Real-time alerts for strong candidates ($0-8/user/month) Google Calendar** - Interview scheduling automation (free) Airtable** - Advanced application tracking (alternative to Sheets) Applicant Portal Integration** - Status updates via API CRM Systems** - Slate, TargetX, Salesforce for higher ed 🚀 Setup Guide (3-4 Hours) Step 1: Create Application Form (60 min) Build comprehensive Jotform with sections: Basic Information Full name, email, phone High school, graduation year Intended major Academic Credentials GPA (weighted/unweighted, scale) SAT score (optional) ACT score (optional) Class rank (if available) Academic honors Essays (Most Important!) Personal statement (650 words max) "Why Our College" essay (250-300 words) Supplemental prompts (program-specific) Activities & Achievements Extracurricular activities (list with hours/week, years) Leadership positions (with descriptions) Honors and awards Community service hours Work experience Additional Information First-generation college student (yes/no) Financial aid needed (yes/no) Optional: demographic information Optional: additional context Step 2: Import n8n Workflow (15 min) Copy JSON from artifact n8n: Workflows → Import → Paste Includes all nodes + 7 detailed sticky notes Step 3: Configure OpenAI API (20 min) Get API key: https://platform.openai.com/api-keys Add to both AI nodes (Essay Analysis + Holistic Review) Model: gpt-4o (best for nuanced analysis) Temperature: 0.3 (consistency with creativity) Test with sample application Cost: $0.15-0.25 per application (essay analysis + holistic review) Step 4: Customize Institutional Context (45 min) Edit AI prompts to reflect YOUR college: In Holistic Review Prompt, Update: College name and type Acceptance rate Average admitted student profile (GPA, test scores) Institutional values and culture Academic programs and strengths What makes your college unique Desired student qualities In Essay Analysis Prompt, Add: Specific programs to look for mentions of Faculty names applicants should reference Campus culture keywords Red flags specific to your institution Step 5: Setup Email Communications (30 min) Connect Gmail/Outlook OAuth Update all recipient addresses: admissions-director@college.edu admissions-committee@college.edu Email addresses for strong candidate alerts Customize email templates: Add college name, logo, branding Update contact information Adjust tone to match institutional voice Include decision release dates Add applicant portal links Step 6: Configure Slack Alerts (15 min, Optional) Create channel: #admissions-strong-candidates Add webhook URL or bot token Test with mock strong candidate Customize alert format and recipients Step 7: Create Admissions Database (30 min) Google Sheet with columns:

Reddit Lead Generator - Frontend Integrated (Productized Version) Overview Production-ready Reddit lead generation system with progressive data loading for optimal UX. This workflow integrates with a web frontend, sending results in real-time as they're processed instead of waiting for everything to complete. Key Features ✅ Progressive Loading - Users see results as they come in (website analysis → keywords → conversations) ✅ 5 Response Stages - Data sent in batches for better UX ✅ Webhook Authentication - Secured with API key headers ✅ Frontend Ready - Built to work with the companion web app ✅ Real-Time Updates - No waiting for 2-minute batch processing What This Workflow Does Receives product URL from frontend via webhook Immediately responds "processing started" Analyzes website with Firecrawl → sends data to frontend Generates 10 keywords with OpenAI → sends to frontend Searches Reddit and filters conversations → sends in 3 batches Frontend displays results progressively as they arrive Response Flow Stage 1: website_analysis - Product details, favicon, summary Stage 2: keywords_generated - All 10 keywords Stage 3: conversations_partial1 - First keyword results Stage 4: conversations_partial2 - Second keyword results Stage 5: conversations_final - Remaining keywords (3-10) in markdown Quick Setup Set Environment Variables in the "Set Environment Variables" node: BACKEND_API_URL - Your frontend API endpoint WEBHOOK_API_KEY - Your webhook security key Configure Credentials: Firecrawl API Reddit OAuth2 OpenAI API Deploy Frontend & Backend: Clone repo: https://github.com/juppfy/leads-gen Deploy frontend on Vercel Deploy backend on Railway Full instructions in the repo README Activate Workflow and update your frontend with the webhook URL Requirements n8n (cloud or self-hosted) Firecrawl API key Reddit Developer Account OpenAI API key Frontend + Backend deployed (see GitHub repo) Resources 📖 Complete Setup Guide: https://bit.ly/mediumarticleredditworkflow 🎥 Video Tutorial: https://bit.ly/youtubetutorialredditworkflow 💻 GitHub Repo: https://github.com/juppfy/leads-gen Frontend Integration This workflow requires the companion web app to receive and display results. The frontend handles: User input and URL validation Real-time result display Conversation cards with pagination Request tracking by searchId Deploy instructions and complete code available in the GitHub repo above. Difference from Batch Version This Version (Frontend): Progressive data loading Real-time updates Production-ready UX Requires frontend deployment Batch Version: Single output at end No frontend needed Perfect for testing/scheduled runs Simpler setup Support Questions? Check the Medium article or YouTube tutorial first. Both have detailed setup instructions and troubleshooting tips.

📊 Description Automate your client proposal creation with this intelligent workflow that transforms Google Sheets entries into professional Google Docs proposals using OpenAI GPT-4o. Designed for agencies and sales teams, it delivers personalized, branded, and structured proposals in minutes — no manual editing required. 🚀📄🤖 What This Template Does Triggers when a new row is added in a connected Google Sheet. 📋 Filters only the latest row to ensure one proposal per new entry. 🔍 Uses GPT-4o to generate structured proposal content (Executive Summary, Scope, Costing, Timeline, Conclusion). 💡 Parses output into validated JSON format for accurate field mapping. ⚙️ Populates a Google Docs template with AI-generated content using placeholders. 📝 Downloads the completed proposal as a PDF file. 💾 Archives the finalized document into a designated Google Drive folder. 📂 Resets the template for the next proposal cycle automatically. 🔄 Key Benefits ✅ Eliminates repetitive manual proposal writing. ✅ Ensures brand consistency with structured templates. ✅ Generates high-quality proposals using AI in real time. ✅ Automates document formatting, saving hours per client. ✅ Scales easily for agencies handling multiple clients daily. Features Google Sheets trigger for new entries. GPT-4o-based content generation with customizable prompts. JSON output validation and structured parsing. Google Docs population using placeholder replacement. Drive storage automation for version tracking. End-to-end automation from data to proposal delivery. Requirements Google Sheets document with columns: clientName, jobDescription. Google Docs template with placeholders (e.g., {{executive_summary}}, {{scope_of_work}}). OpenAI API key (GPT-4o). Google Drive credentials for output management. Target Audience Marketing and web agencies automating client proposal generation. Sales teams preparing project estimates and deliverables. Freelancers and consultants managing multiple client requests. Businesses streamlining documentation workflows. Step-by-Step Setup Instructions Connect Google Sheets and replace the Sheet ID placeholder. Set up your Google Docs proposal template and replace the Document ID. Add your OpenAI API key for GPT-4o content generation. Specify your Google Drive folder for saving proposals. Test the workflow with a sample entry to confirm formatting. Activate the workflow for continuous proposal generation. ✅

How It Works This workflow automates automotive regulatory compliance evaluation by intelligently routing assessments through parallel evaluation paths based on component type. Designed for automotive compliance officers, quality assurance teams, and regulatory affairs managers, it solves the complex challenge of ensuring vehicle components meet diverse regulatory standards across safety, emissions, and performance requirements. The system receives compliance evaluation requests via webhook, determines whether components require split assessment or integrated regulatory database checks, then processes each path using OpenAI-powered compliance agents with specialized tools for performance simulation and structured output parsing. Results are aggregated, risk scores calculated using business rules, enriched with compliance metadata, and logged to regulatory databases while responding to the originating system with actionable compliance status and required remediation actions. Setup Steps Configure webhook endpoint URL for compliance evaluation system integration Set up OpenAI API credentials for Automotive Compliance Agent access Configure Check Evaluation Type node with component classification rules Set up Fetch Regulatory Database node with regulatory standards API credentials Update Performance Simulation Tool with automotive testing parameters Configure Calculator node with compliance scoring algorithms Customize Structured Output Parser for regulatory reporting format requirements Prerequisites Active OpenAI API account, automotive compliance evaluation system with webhook capability Use Cases Pre-production component compliance validation, supplier part certification Customization Modify compliance agent prompts for region-specific regulations, adjust risk scoring thresholds Benefits Accelerates compliance evaluation by 70%, ensures systematic multi-regulation assessment

This workflow is built for digital marketers, sales professionals, influencer agencies, and entrepreneurs who want to automate Instagram lead generation. If you’re tired of manually searching for profiles, copying email addresses, and updating spreadsheets, this automation will save you hours every week. It turns your process into a smart system that finds, extracts, and stores leads while you focus on growing your business. How it works / What it does This n8n automation completely transforms how you collect Instagram leads using AI and API integrations. Here’s a simple breakdown of how it works: Set your targeting parameters using the Edit Fields node. You can specify your platform (Instagram), field of interest such as “beauty & hair,” and target country like “USA.” Generate intelligent search queries with an AI Agent powered by GPT-4o-mini. It automatically creates optimized Google search queries to find relevant Instagram profiles in your chosen niche and location. Extract results from Google using Apify’s Google Search Scraper, which collects hundreds of Instagram profile URLs that match your search criteria. Fetch detailed Instagram profile data using Apify’s Instagram Scraper. This includes usernames, follower counts, and profile bios where contact information usually appears. Use AI to extract emails from the profile biographies with the Information Extractor node powered by GPT-3.5-turbo. It identifies emails even when they are hidden or creatively formatted. Store verified leads in a PostgreSQL database. The workflow automatically adds new leads or updates existing ones with fields like username, follower count, email, and niche. Once everything is set up, the system runs on autopilot and keeps building your database of quality leads around the clock. How to set up Follow these steps to get your Instagram Lead Generation Machine running: Import the JSON file into your n8n instance. Add your API credentials: Apify token for the Google and Instagram scrapers OpenAI API key for the AI-powered nodes PostgreSQL credentials for storing leads Open the Edit Fields node and set your platform, field of interest, and target country. Run the workflow manually using the Manual Trigger node to test it. Once confirmed, replace the manual trigger with a schedule or webhook to run it automatically. Check your PostgreSQL database to ensure the leads are being saved correctly. Requirements Before running the workflow, make sure you have the following: An n8n account or instance (self-hosted or n8n Cloud) An Apify account for accessing the Google and Instagram scrapers OpenAI API access for generating smart search queries and extracting emails A PostgreSQL database to store your leads Basic understanding of how n8n workflows and nodes operate How to customize the workflow This workflow is flexible and can be customized to fit your business goals. Here’s how you can tailor it: Change your niche or location by updating the Edit Fields node. You can switch from “beauty influencers in the USA” to “fitness coaches in Canada” in seconds. Add more data fields to collect additional information such as engagement rates, bio keywords, or profile categories. Just modify the PostgreSQL node and database schema. Connect to your CRM or email system to automatically send introduction emails or add new leads to your marketing pipeline. Use different triggers such as a scheduled cron trigger for daily runs or a webhook trigger to start the workflow through an API call. Filter higher-quality leads by adding logic to capture only profiles with a minimum number of followers or verified emails.