OpenAI and Postgres integration

Video Guide I prepared a detailed guide explaining how to build an AI-powered meeting assistant that provides real-time transcription and insights during virtual meetings. Youtube Link Who is this for? This workflow is ideal for business professionals, project managers, and team leaders who require effective transcription of meetings for improved documentation and note-taking. It's particularly beneficial for those who conduct frequent virtual meetings across various platforms like Zoom and Google Meet. What problem does this workflow solve? Transcribing meetings manually can be tedious and prone to error. This workflow automates the transcription process in real-time, ensuring that key discussions and decisions are accurately captured and easily accessible for later review, thus enhancing productivity and clarity in communications. What this workflow does The workflow employs an AI-powered assistant to join virtual meetings and capture discussions through real-time transcription. Key functionalities include: Automatic joining of meetings on platforms like Zoom, Google Meet, and others with the ability to provide real-time transcription. Integration with transcription APIs (e.g., AssemblyAI) to deliver seamless and accurate capture of dialogue. Structuring and storing transcriptions efficiently in a database for easy retrieval and analysis. Real-Time Transcription: The assistant captures audio during meetings and transcribes it in real-time, allowing participants to focus on discussions. Keyword Recognition: Key phrases can trigger specific actions, such as noting important points or making prompts to the assistant. Structured Data Management: The assistant maintains a database of transcriptions linked to meeting details for organized storage and quick access later. Setup Preparation Create Recall.ai API key Setup Supabase account and table create table public.data ( id uuid not null default gen_random_uuid (), date_created timestamp with time zone not null default (now() at time zone 'utc'::text), input jsonb null, output jsonb null, constraint data_pkey primary key (id), ) tablespace pg_default; Create OpenAI API key Development Bot Creation: Use a node to create the bot that will join meetings. Provide the meeting URL and set transcription options within the API request. Authentication: Configure authentication settings via a Bearer token for interacting with your transcription service. Webhook Setup: Create a webhook to receive real-time transcription updates, ensuring timely data capture during meetings. Join Meeting: Set the bot to join the specified meeting and actively listen to capture conversations. Transcription Handling: Combine transcription fragments into cohesive sentences and manage dialog arrays for coherence. Trigger Actions on Keywords: Set up keyword recognition that can initiate requests to the OpenAI API for additional interactions based on captured dialogue. Output and Summary Generation: Produce insights and summary notes from the transcriptions that can be stored back into the database for future reference.

The AI-powered MIS Agent is an intelligent, automated system built using n8n that streamlines email-based data collection and document organization for businesses. It classifies incoming emails, extracts and processes attachments or Drive links, and routes them to the correct destination folders in Google Drive. Additionally, it provides advanced file operations like cleaning, merging, joining, and transforming data. Advantages 📥 Automated Email and File Management Detects and processes emails containing attachments or Drive links, ensuring seamless classification and routing of business-critical files. 🧠 AI-Based Classification Uses LLMs (like GPT-4o Mini) to classify emails into categories such as Daily Sales, Customer Info, and Address based on their content. 📂 Smart File Routing and Upload Recognizes whether a file is a direct attachment or a Google Drive link, extracts the file ID if necessary, and uploads it to predefined folders. 📊 Powerful Data Operations Supports operations like append, join, group by, aggregation, and standardization of data directly from spreadsheets using Python and Pandas within the workflow. 🔁 Scheduled and Triggered Automation Supports scheduled runs and real-time email triggers, making it highly reliable and timely. 🔧 Fully Modular and Scalable Easily expandable with more logic, new folders, or different workflows. Clean architecture and annotations make maintenance simple. How It Works Email Trigger The system uses a Gmail trigger to monitor incoming emails with specific labels or attachments. Classification An LLM-based text classifier identifies the purpose of the email (e.g., sales data, address list, customer details). Conditional Logic Regex-based conditions check if the email contains Google Drive links or attachments. File Handling If it's a Drive link, it extracts the file ID and copies it to the correct folder. If it's an attachment, it uploads directly. Scheduled Data Management Periodically moves or logs files from predefined folders using a schedule trigger. Data Cleaning and Processing Performs data cleaning and transformation tasks like replacing missing values, standardizing formats, and joining datasets based on criteria provided by the user. Final Output Cleaned and processed files are saved in designated folders with their public links shared back through the system. Set Up Steps Configure Nodes: Gmail Trigger: Detects relevant incoming emails. Text Classifier: Uses OpenAI model to categorize email content. Regex Conditions: Determine whether a link or attachment is present. Google Drive Operations: Upload or copy files to categorized folders. Python Nodes: Handle data manipulation using Pandas. Google Sheets Nodes: Extract, clean, and write structured data. LLM-based Chat Models: Extract and apply cleaning configurations. Connect Nodes: Seamlessly connect Gmail inputs, classification, file processing, and data logic. Output links or processed files are uploaded back to Drive and ready to share. Credentials: Ensure OAuth credentials for Gmail, Google Drive, and OpenAI are correctly set. Ideal For Sales & CRM teams managing large volumes of email-based reports. Data teams needing structured pipelines from unstructured email inputs. Businesses looking to automate classification, storage, and transformation of routine data. Testing and Additional customization If you want to test this bot capability before purchasing the workflow. ask me on my mail [email protected] I will share the chat url and the links of associated google drives to see the result once you are satisfied then we are good to go. I have just kept $1 for testing purposes because of paid open ai . -If there is any customization needed like charts and other request like adding databases feel free to let me know i can do it accordingly. This is the first version i will come with more advancements based on the request and responses. Use it and let me know on [email protected]

AI-Powered WhatsApp Assistant for Restaurant & Delivery – Automate Orders, Calculate Delivery Fees & Enhance Customer Service with n8n 📌 Optimize your restaurant's delivery process with AI-powered automation! This n8n workflow enables an intelligent WhatsApp assistant for restaurants, burger joints, and delivery businesses. The AI chatbot efficiently handles orders, calculates delivery fees based on distance, manages customer interactions, and seamlessly integrates with your CRM and database for streamlined operations. ⚠️ Important Notice: This template is only compatible with self-hosted n8n instances as it uses community nodes. Ensure secure credential management when configuring APIs. 🔹 Key Features ✅ AI-Powered Order Processing – Automatically receive and confirm orders via WhatsApp. ✅ Real-Time Distance Calculation – Determines the delivery distance and calculates the fee dynamically. ✅ Automated Customer Interaction – Engages customers with intelligent conversation and order updates. ✅ WhatsApp Integration – Processes messages, saves customer details, and retrieves past interactions. ✅ Secure Customer Data Storage – Saves order history, customer preferences, and location details in a database. ✅ Seamless CRM & POS System Integration – Sync orders and customer data with your existing platforms. 📌 How It Works 1️⃣ A customer sends a message via WhatsApp to start an order. 2️⃣ The AI assistant guides them through the menu, recommends items, and confirms their choices. 3️⃣ The system retrieves and saves customer details (name, phone number, and address). 4️⃣ The AI calculates the delivery distance and applies the appropriate delivery fee. 5️⃣ The order details are saved and forwarded to the restaurant’s system for preparation. 6️⃣ The assistant keeps the customer updated about the order status. ⚙️ Setup & Customization 🔧 Webhook for WhatsApp – Capture and process customer messages automatically. 🔧 OpenAI-Powered AI Assistant – Configure conversation flows for natural and engaging interactions. 🔧 Delivery Fee Calculation – Set your delivery price per kilometer and adjust base rates. 🔧 Database & CRM Integration – Store customer details, order history, and location data. 🔧 Customizable Order Flow – Adapt the workflow for different restaurant models. 🔧 Secure Credential Management – Store API keys safely to prevent unauthorized access. 💡 Requirements: This template is recommended for users with basic knowledge of n8n. If you need custom development or setup assistance, contact us via WhatsApp: +55 17 99155-7874. 🚀 Automate order processing, reduce manual tasks, and improve customer satisfaction with AI-powered WhatsApp automation! Assistente de WhatsApp com IA para Restaurantes & Delivery – Automatize Pedidos, Calcule Taxas de Entrega e Melhore o Atendimento ao Cliente com n8n 📌 Otimize o processo de entrega do seu restaurante com automação baseada em IA! Este fluxo para n8n permite criar um atendente virtual inteligente no WhatsApp, ideal para hamburguerias, restaurantes e serviços de delivery. O chatbot baseado em IA recebe pedidos, calcula a taxa de entrega com base na distância, interage com os clientes e integra-se ao seu CRM para operações mais eficientes. ⚠️ Aviso Importante: Este template é compatível apenas com instâncias auto-hospedadas do n8n, pois utiliza nós da comunidade. Certifique-se de gerenciar credenciais de forma segura ao configurar APIs. 🔹 Principais Funcionalidades ✅ Processamento Inteligente de Pedidos – Recebe e confirma pedidos automaticamente via WhatsApp. ✅ Cálculo Dinâmico da Taxa de Entrega – Mede a distância e aplica a taxa correta automaticamente. ✅ Atendimento Automatizado ao Cliente – Interage com clientes de forma natural e profissional. ✅ Integração com WhatsApp – Captura mensagens, armazena dados e recupera histórico de pedidos. ✅ Armazenamento Seguro de Dados do Cliente – Salva pedidos, preferências e endereços de entrega. ✅ Sincronização com CRM e Sistema de PDV – Conecta-se ao seu sistema de gestão para um fluxo contínuo. 📌 Como Funciona 1️⃣ O cliente envia uma mensagem via WhatsApp para iniciar um pedido. 2️⃣ O assistente de IA apresenta o menu, sugere itens e confirma a escolha do cliente. 3️⃣ O sistema captura e salva os dados do cliente (nome, telefone e endereço). 4️⃣ A IA calcula a distância de entrega e aplica a taxa de acordo com a localização. 5️⃣ Os detalhes do pedido são registrados e enviados para a cozinha. 6️⃣ O assistente atualiza o cliente sobre o status do pedido. ⚙️ Configuração e Personalização 🔧 Webhook para WhatsApp – Configuração para capturar e processar pedidos automaticamente. 🔧 Assistente de IA com OpenAI – Configuração de fluxos conversacionais naturais e envolventes. 🔧 Cálculo de Taxa de Entrega – Definição de valores por quilômetro e ajustes de tarifas base. 🔧 Banco de Dados & CRM – Armazena dados de clientes, histórico de pedidos e localização. 🔧 Fluxo de Pedidos Personalizável – Adapte para diferentes tipos de restaurantes e serviços de delivery. 🔧 Gerenciamento Seguro de Credenciais – Proteja suas chaves de API contra acessos não autorizados. 💡 Requisitos: Este template é recomendado para usuários que já possuem conhecimentos básicos em n8n. Se precisar de personalização ou suporte para instalação, entre em contato pelo WhatsApp: +55 17 99155-7874. 🚀 Automatize o processamento de pedidos, reduza tarefas manuais e melhore a satisfação dos clientes com um WhatsApp automatizado e inteligente!

🐶🤖 AI Agent for Pet Shops – Automate Customer Service & Bookings! 🐾💡 Transform Your Pet Shop with AI-Powered Automation! 🚀 Enhance customer experience and optimize operations with this n8n AI Agent designed for pet shops. 📲🐾 Automate client interactions, appointment scheduling, and service recommendations—saving time and increasing revenue! 🔹 Key Features: ✅ Instant WhatsApp responses – AI-powered chatbot handles customer inquiries. 💬 ✅ Automated appointment scheduling – Clients can book services hassle-free. 📅✂️ ✅ Personalized reminders – Reduce no-shows with automated notifications. 📢🐾 ✅ Customer data & service history management – Track interactions effortlessly. 📊📁 ✅ Product & service recommendations – Improve sales with smart suggestions. 🎁🐶 📌 How It Works 1️⃣ The workflow captures customer inquiries via WhatsApp. 2️⃣ AI processes requests, provides information, and offers booking options. 3️⃣ Clients can schedule grooming, vet visits, or other services in seconds. 4️⃣ Automated reminders ensure appointments are remembered. 5️⃣ Customer data is stored for better service personalization. ⚙️ Setup & Customization 🔧 Connect your WhatsApp API (evolution) for instant messaging. 🔧 Integrate with Google Calendar for appointment booking. 🔧 Customize reminders, services, and pricing rules to fit your business. 💡 Reduce manual work, improve customer satisfaction, and scale your pet shop with AI automation! 🐶🤖 [PT-BR] Agente de IA para Pet Shops – Atendimento e Agendamentos Automatizados! 🐾💡 Transforme Seu Pet Shop com Automação Inteligente! 🚀 Otimize o atendimento ao cliente e agilize processos com este Agente de IA para n8n. 📲🐾 Automatize interações, agendamentos e recomendações de serviços—economizando tempo e aumentando as vendas! 🔹 Principais Funcionalidades: ✅ Atendimento automático no WhatsApp – IA responde clientes instantaneamente. 💬 ✅ Agendamento de serviços automatizado – Clientes marcam banho, tosa ou consultas facilmente. 📅✂️ ✅ Lembretes personalizados – Reduza faltas com notificações automáticas. 📢🐾 ✅ Gestão de clientes e histórico de serviços – Controle dados de forma eficiente. 📊📁 ✅ Sugestão de produtos e serviços – Venda mais com recomendações inteligentes. 🎁🐶 📌 Como Funciona 1️⃣ O fluxo recebe perguntas dos clientes via WhatsApp. 2️⃣ A IA processa os pedidos e fornece opções de agendamento. 3️⃣ O cliente escolhe o serviço desejado e agenda em segundos. 4️⃣ Lembretes automáticos garantem que os clientes não esqueçam os horários. 5️⃣ O histórico do cliente é salvo para oferecer um atendimento mais personalizado. ⚙️ Configuração e Personalização 🔧 Conecte sua API do WhatsApp (evolution) para interação automática. 🔧 Integre ao Google Calendar para gerenciar agendamentos. 🔧 Personalize valores, serviços e regras de envio de lembretes conforme sua necessidade. 💡 Automatize processos, melhore a experiência do cliente e escale seu pet shop com IA! 🚀

How it works This workflow automates customer communication after an order is placed. It detects new orders, sends a confirmation email, waits until delivery is completed, and then follows up with AI-generated product tips. Finally, it sends AI-powered upsell recommendations after a defined delay. The workflow runs on a schedule and requires no manual handling. Step-by-step Step 1 — Order detection and confirmation** Schedule Trigger – Runs at a fixed interval to check for new orders. Postgres – Execute a SQL query – Fetches recently created orders from the database. Gmail – Send message – Sends an order confirmation email to the customer. Step 2 — Delivery status monitoring** Wait – Pauses the workflow until the expected delivery window. Postgres – Select rows – Checks the delivery status of the order. If – Verifies whether the product is marked as delivered. Wait – Delays one day and repeats the check if delivery is not completed. Step 3 — Product usage tips (AI)** OpenAI – Message a model – Generates short and practical usage tips for the product. Code – Formats the AI output into clean HTML bullet points. Gmail – Send message – Emails the usage tips to the customer. Step 4 — Upsell recommendations (AI)** Wait – Delays for two weeks after tips are sent. OpenAI – Message a model – Creates complementary product recommendations. Code – Formats the recommendations for email delivery. Gmail – Send message – Sends the upsell email to the customer. Why use this? Ensures timely and relevant customer communication Prevents sending tips before the product is delivered Improves onboarding with AI-generated usage guidance Increases repeat purchases through smart upsell emails Fully automated using database, email, and AI integrations

Monitor employee stress levels from Slack and tasks to Google Sheets This workflow functions as an automated "Chief Wellness Officer," helping HR teams and managers prevent employee burnout before it happens. It aggregates data from communication channels and work tools to provide an AI-driven daily assessment of employee well-being, while prioritizing privacy through data anonymization. Who is this for HR & People Operations Teams** wanting to track organizational health trends without invading personal privacy. Remote-first Companies** where physical cues of burnout are harder to spot. Team Managers** looking for data-backed insights to support their direct reports. What it does Daily Trigger: Runs automatically every morning (default: 2 AM) to analyze the previous day's activity. Data Collection: Fetches public Slack messages to analyze communication tone. Retrieves attendance data (work hours, late arrivals) and task completion rates via API. AI Analysis: Uses OpenAI to process the data, predicting a "Stress Level" based on sentiment and workload metrics. Privacy-First Reporting: Hashes employee IDs to anonymize data, then logs stress scores to Google Sheets for trend analysis. Direct Intervention: If the AI detects "High" stress, it sends a private, empathetic DM to the employee on Slack offering support or counseling resources. Requirements n8n** (Self-hosted or Cloud) Slack** Workspace (with a Bot User OAuth Token). OpenAI** API Key. Google Sheets** (for the dashboard). Postgres** (optional, for logging counseling actions). HR/Task Management Tools** (e.g., Jira, Asana, BambooHR) accessible via API. How to set up Configure Credentials: Set up your credentials for Slack, OpenAI, Google Sheets, and Postgres in n8n. Prepare Google Sheet: Create a sheet with headers: employee_hash, department, stress_score, stress_level, and analysis_date. Connect Data Sources: The workflow uses HTTP Request nodes as placeholders for Attendance and Task data. You must update these URLs to point to your specific HRIS or Project Management tool APIs (or replace them with native n8n nodes like Asana or Jira). Update Slack Settings: Ensure the Slack node has the correct permissions (channels:history, chat:write, users:read) and target channel/user mapping. How to customize Adjust Sensitivity:* Modify the system prompt in the *AI Stress Level Prediction node to change how "High" stress is defined. Change Data Sources:* Replace the generic HTTP Request nodes with specific n8n nodes for services like Harvest, *Jira, or Trello depending on your stack. Modify Action:** Instead of a direct DM, you can change the final step to alert a manager or create a ticket in a sensitive HR service desk.

Description This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI-powered diagnostics, and alerts your team only when real issues occur. Notion holds all data quality rules (source, field, condition, severity). n8n reads them on schedule, converts them into live SQL queries, and aggregates anomalies into a global run summary. The workflow then scores data health, creates a Notion run record, optionally opens a Jira issue, and sends a Slack/email alert including AI-generated root cause & recommended fixes. Target users Perfect for: DataOps Analytics Product Data BI Compliance ETL/ELT pipelines Platform reliability teams. Workflow steps How it works 1) Notion → Rules Database Each entry defines a check (table, field, condition, severity). 2) n8n → Dynamic Query Execution Rules are converted into SQL and checked automatically. 3) Summary Engine Aggregates anomalies, computes data quality score. 4) AI Diagnostic Layer Root cause analysis + recommended fix plan. 5) Incident Handling Notion Run Page + optional Slack/Email/Jira escalation. Silent exit when no anomaly = zero noise. Setup Instructions Create two Notion databases: Data Quality Rules → source / field / rule / severity / owner Data Quality Runs → run_id / timestamp / score / anomalies / trend / AI summary/recommendation Connect SQL database (Postgres / Supabase / Redshift etc.) Add OpenAI credentials for AI analysis Connect Slack + Gmail + Jira for incident alerts Set your execution schedule (daily/weekly) Expected outcomes Fully automated, rule-based data quality monitoring with minimal maintenance and zero manual checking. When everything is healthy, runs remain silent. When data breaks, the team is notified instantly: with context, root cause insight, and a structured remediation output. Tutorial video Watch the Youtube Tutorial video About me : I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Description This workflow transforms raw SaaS metrics into a fully automated Product Health Monitoring & Incident Management system. It checks key revenue and usage metrics every day (such as churn MRR and feature adoption), detects anomalies using a statistical baseline, and automatically creates structured incidents when something unusual happens. When an anomaly is found, the workflow logs it into a central incident database, alerts the product team on Slack and by email, enriches the incident with context and AI-generated root-cause analysis, and produces a daily health report for leadership. It helps teams move from passive dashboard monitoring to a proactive, automated system that surfaces real issues with clear explanations and recommended next steps. Context Most SaaS teams struggle with consistent product health monitoring: Metrics live in dashboards that people rarely check proactively Spikes in churn or drops in usage are noticed days later There is no unified system to track, investigate, and report on incidents Post-mortems rely on memory rather than structured data Leadership often receives anecdotal updates instead of reliable daily reporting This workflow solves that by: Tracking core health metrics daily (revenue and usage) Detecting anomalies based on recent baselines, not arbitrary thresholds Logging all incidents in a consistent format Notifying teams only when action is needed Generating automated root-cause insights using AI + underlying database context Producing a daily “Product Health Report” for decision-makers The result: Faster detection, clearer understanding, and better communication across product, growth, and leadership teams. Target Users This template is ideal for: Product Managers & Product Owners SaaS founders and early-stage teams Growth, Analytics, and Revenue Ops teams PMO / Operations teams managing product performance Any organization wanting a lightweight incident monitoring system without building internal tooling Technical Requirements You will need: A Postgres / Supabase database containing your product metrics Slack credentials for alerts Gmail credentials for email notifications (Optional) Notion credentials for incident documentation and daily reports An OpenAI / Anthropic API key for AI-based root cause analysis Workflow Steps The workflow is structured into four main sections: 1) Daily Revenue Health Runs once per day, retrieves recent revenue metrics, identifies unusual spikes in churn MRR, and creates incidents when needed. If an anomaly is detected, a Slack alert and email notification are sent immediately. 2) Daily Usage Health Monitors feature usage metrics to detect sudden drops in adoption or engagement. Incidents are logged with severity, context, and alerts to the product team. 3) Root Cause & Summary For every open incident, the workflow: Collects additional context from the database (e.g., churn by country or plan) Uses AI to generate a clear root cause hypothesis and suggested next steps Sends a summarized report to Slack and email Updates the incident status accordingly 4) Daily Product Health Report Every morning, the workflow compiles all incidents from the previous day into: A daily summary email for leadership A Notion page for documentation and historical tracking This ensures stakeholders have clear visibility into product performance trends. Key Features Automated anomaly detection across revenue and usage metrics Centralized incident logging with metadata and raw context Severity scoring based on deviation from historical baselines Slack and email alerts for fast response AI-generated root cause analysis with recommended actions Daily product health reporting for leadership and PM teams Optional Notion integration for incident documentation System logging for observability and auditability Fully modular: you can add more metrics, alert channels, or analysis steps easily Expected Output When running, the workflow will generate: Structured incident records in your database Slack alerts for revenue or usage anomalies Email notifications with severity, baseline vs actual, and context AI-generated root cause summaries A daily health report summarizing all incidents (Optional) Notion pages for both incidents and daily reports System logs recording successful executions Tutorial video: Watch the Youtube Tutorial video About me I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

How It Works A scheduled process aggregates content from eight distinct data sources and standardizes all inputs into a unified format. AI models perform sentiment scoring, detect conspiracy or misinformation signals, and generate trend analyses across domains. An MCDN routing model prioritizes and channels insights to the appropriate workflows. Dashboards visualize real-time analytics, trigger KPIs based on thresholds, and compile comprehensive market-intelligence reports for stakeholders. Setup Steps Data Sources: Connect news APIs, social media platforms, academic databases, code repositories, and documentation feeds. AI Analysis: Configure OpenAI models for sentiment analysis, conspiracy detection, and trend scoring. Dashboards: Integrate analytics platforms and enable automated email or reporting outputs. Storage: Configure a database for historical records, trend archives, and competitive-intelligence storage. Prerequisites Multi-source API credentials; OpenAI API key; dashboard platform access; email service; code repository access; academic database credentials Use Cases Competitive intelligence monitoring; market trend analysis; technology landscape tracking; product strategy research; misinformation filtering Customization Adjust sentiment thresholds; add/remove data sources; modify analysis rules; extend AI models Benefits Reduces research time 80%; consolidates market intelligence; improves decision accuracy

How It Works Scheduled processes retrieve customer feedback from multiple channels. The system performs sentiment analysis to classify tone, then uses OpenAI models to extract themes, topics, and urgency indicators. All processed results are stored in a centralized database for trend tracking. Automated rules identify high-risk or negative sentiment items and trigger alerts to the relevant teams. Dashboards and workflow automation then visualize insights and support follow-up actions. Setup Instructions Data Sources: Connect social media APIs, survey tools, and customer support platforms. AI Analysis: Configure the OpenAI API with sentiment and theme-extraction prompts. Database: Set up a feedback storage schema in your utility database. Alerts: Configure email notifications and CRM triggers for priority issues. Dashboards: Link your analytics and reporting tools for real-time insights. Prerequisites Social media/survey API credentials; OpenAI API key; database access; CRM system credentials; email notification setup Use Cases Customer sentiment tracking; product feedback aggregation; support ticket prioritization; brand monitoring; trend identification Customization Adjust sentiment thresholds; add new feedback sources; modify categorization rules Benefits Reduces analysis time 85%; captures actionable insights; enables rapid response to issues

This n8n workflow lets you delegate inbox management to an AI agent. It automatically replies to emails when a suitable response is found in your knowledge base. If no match is found, the message is forwarded to a human for review. This ensures all replies follow company guidelines and prevent misinformation or misrepresentation. Use Case Ideal for businesses that receive frequent inquiries about products or services and want to automate responses, freeing up time to focus on core operations. How It Works Polls your inbox for new incoming emails Cleans HTML to reduce token usage and filters spam Categorizes emails for better organization Retrieves conversation history from Supabase (if available) to give the AI proper context Uses the AI agent to query your FAQ database for relevant answers Replies automatically if a suitable answer is found Sends a placeholder email and forwards to a human if no accurate answer exists Ingests all processed emails into Supabase to retain conversation history and improve future AI accuracy Customization You can connect any email provider or LLM model. The workflow can also be expanded to let the AI schedule meetings, answer availability questions, or handle other inbox tasks. If you'd like help tailoring this workflow to your business, feel free to reach out: Upwork Profile

Who’s It For This workflow is ideal for HR professionals, recruiters, and small businesses looking to streamline resume screening with AI-powered analysis and CRM integration. What It Does This template automates resume processing by: Capturing resume submissions (name, email, PDF) via JotForm. Converting PDFs to images using PDF.co API. Extracting text with Azure Vision OCR. Analyzing resumes with GPT-4.1 for strengths, improvements, and a score (1–100). Storing submission data in PostgreSQL. Adding high-scoring resumes (>85) to Zoho CRM and sending congratulatory emails. Sending feedback emails for lower-scoring resumes. How to Set Up Configure JotForm Trigger: Add your JotForm API key and form ID (e.g., 252434958811059). Set Up PostgreSQL: Create a resume table with columns: id (SERIAL PRIMARY KEY), given_name (VARCHAR), given_email (VARCHAR), resume_loc (VARCHAR). Add Credentials: Store API keys for PDF.co, Azure Vision OCR, OpenAI, Zoho CRM, and Gmail in n8n’s credential system. Test the Workflow: Submit a test resume via JotForm and verify data flow through each node. Requirements n8n instance (cloud or self-hosted). Accounts with JotForm, PDF.co, Azure Vision, OpenAI, Zoho CRM, and Gmail. PostgreSQL database. How to Customize Adjust the GPT-4.1 prompt for specific job roles. Modify the score threshold (currently 85) in the "if score > 58?" node. Update email templates for personalized messaging. PostgreSQL Table Structure > Node to create table included in workflow.

How it works User Signup & Verification: The workflow starts when a user signs up. It generates a verification code and sends it via SMS using Twilio. Code Validation: The user replies with the code. The workflow checks the code and, if valid, creates a session for the user. Conversational AI: Incoming SMS messages are analyzed by Chat GPT AI for sentiment, intent, and urgency. The workflow stores the conversation context and generates smart, AI-powered replies. Escalation Handling: If the AI detects urgency or frustration, the workflow escalates the session—alerting your team and sending a supportive SMS to the user. Set up steps Estimated setup time:** 10–20 minutes for most users. What you’ll need:** A free n8n account (self-hosted or cloud) Free Twilio account (for SMS) OpenAI API key (for AI) A PostgreSQL database (Supabase, Neon, or local) Setup process:** Import this workflow into n8n. Add your Twilio and OpenAI credentials as environment variables or n8n credentials. Update webhook URLs in your Twilio console (for incoming SMS). (Optional) Adjust sticky notes in the workflow for detailed, step-by-step guidance.

✍️ AI-Powered SEO Blog Automation Workflow Complete PostgreSQL-backed system: Keyword scoring → AI research → Multi-part content generation → fal.ai Nano Banana image generation → WordPress publishing Real Example: AI Applications in Real Estate Playbook — fully generated, optimized, and published with this automation. 🚀 What You Get Full n8n Workflow Package Pre-configured automation** with 80+ connected nodes PostgreSQL schema** for keyword tracking & blog management 3-phase system**: Scoring → Planning → Generation Setup documentation** with customization guide Each Blog Post Includes 2,100+ words across 6 sections (Intro, Dev 1, Dev 2, Conclusion, FAQ, Header) 2 AI-generated images via fal.ai Nano Banana (16:9 format) Internal links to your existing content External citations to authoritative sources Complete SEO metadata (title, slug, description, tags) WordPress-ready draft ⚡ Core Features Keyword Intelligence Multi-factor scoring algorithm (volume, competition, CPC, relevance) Usage tracking prevents cannibalization AI agent selects optimal clusters avoiding impossible competition AI Content Pipeline Perplexity Sonar Pro deep research GPT-5 parallel section generation Grade 13-15 reading level Natural keyword distribution (no over-optimization) Image Generation fal.ai Nano Banana model** for contextual visuals Featured image (16:9) from research summary Body image (16:9) embedded in content Automatic alt text generation 10s wait + 5s retry logic Automation Infrastructure Queries existing posts to prevent duplicates Scheduled execution (every 2-3 days) Error handling with retry logic Non-blocking image generation 🔗 Required Services OpenAI (GPT-5, GPT-4o) OpenRouter (GPT-5 Mini) Perplexity AI (Sonar Pro) fal.ai (Nano Banana model)** WordPress REST API PostgreSQL database n8n instance 📊 Database Schema Keywords Table - Tracks scoring, usage, and Google Ads metrics Cluster Blog Table - Manages topic pipeline and published URLs Full SQL schema included in setup documentation. 🎯 Production Ready 10-15 posts/week** on autopilot 60% faster** than sequential generation (parallel processing) Usage tracking** prevents keyword reuse Customizable** scoring weights and relevance terms for any niche 🛠️ Setup Process Import n8n JSON workflow Create PostgreSQL tables (schema provided) Add API credentials Import keywords from Google Ads Planner Customize relevance terms for your industry Configure schedules and domain URL Test manually → Enable automation 💡 Perfect For Content marketers scaling to 10+ posts/week SEO agencies managing multiple sites Niche site builders needing consistent output WordPress users wanting hands-off publishing Low-authority sites targeting winnable keywords 🔧 Key Customization Points Scoring algorithm weights (volume/competition/CPC/relevance) Relevance terms for your industry (default: AI/automation) Publishing schedule (daily/3x week/weekly) Content length per section Image aspect ratio and format Website domain URL Stop writing blogs manually. This workflow handles research, writing, fal.ai Nano Banana image generation, linking, and publishing while you focus on strategy.

Overview of the n8n Workflow This n8n workflow automates the transformation of spreadsheet data into professional charts and graphs using AI-driven analysis. Triggered via Slack, it processes uploaded files (Excel, CSV, Google Sheets, or Drive links), interprets the data with an AI agent to determine the best visualization type (e.g., bar, line, pie, doughnut, or bubble charts), generates images via QuickChart, uploads them to Google Drive, and delivers the results back to the user in Slack with titles and shareable links. It maintains conversation context in Postgres for seamless multi-turn interactions and handles audio or text inputs for chart requests. What this workflow does: Hooks up to Slack for seamless spreadsheet uploads Automatically extracts your data and generates bar charts, line graphs, bubble charts, and more Delivers stunning visualizations straight back to you in Slack Makes it easy to spot trends, patterns, and insights—on demand How it Works • Users upload spreadsheets or share links via Slack, along with a natural language request (e.g., "Create a bar chart of sales by month"). • The workflow detects file types, extracts and aggregates data, then uses an AI agent to parse the request and select an appropriate chart type. • Data is formatted and sent to QuickChart's API to generate the visualization image. • Images are uploaded to Google Drive, and a confirmation message with titles and links is sent back to Slack. Set Up Steps Setup takes about 15-30 minutes, mainly for credential configuration. Detailed node instructions are in the workflow's sticky notes—focus on pasting API keys and testing triggers. Once cloned, the workflow runs plug-and-play; only tweak credentials and optional prompts as needed. ⚙️ Turn Spreadsheets Into Charts & Graphics Automate turning uploaded spreadsheets into AI-generated charts (bar, line, pie, etc.) via Slack, with results shared as Google Drive links. 🧑‍💻 Author: LeeWei 🚀 Steps to Connect: Slack Bot Setup Create a Slack app at api.slack.com/apps and add scopes for chat:write, files:read, channels:read. Generate a Bot User OAuth Token and paste it into the Slack Trigger node's credentials in n8n. Invite the bot to your desired channel for file uploads and messages. OpenAI API Key Sign up at platform.openai.com and generate an API key. Paste this key into the OpenAI (gpt-4o-mini) node's credentials. 💡 For cost efficiency, monitor usage—basic charts use minimal tokens. Postgres Database Connection Set up a Postgres instance (e.g., via Supabase or your host) with a table named n8n_rodger_chat for chat history. Add connection details (host, database, user, password) to the Postgres nodes' credentials. This enables thread memory; skip if not using multi-turn chats (but recommended for context). Google Sheets & Drive Setup Create OAuth2 credentials at console.cloud.google.com with scopes for Sheets (read) and Drive (upload, share). Paste the credentials into the Google Sheets and implied Drive upload nodes. Test by sharing a sample sheet—ensures data extraction and image storage work. QuickChart Integration No API key needed for free tier (up to 500 charts/month); visit quickchart.io to confirm. For production (100k+ charts/month), upgrade to corporate plan ($40/month) and add any auth if required in the HTTP Request node. The node is pre-configured for chart generation—edit URL params only for custom styling. Plug and Play Instructions Clone the workflow JSON directly into n8n—all nodes (triggers, AI agents, extractors, switches) are pre-wired and ready. No re-setup needed beyond the steps above. Key editable fields (found in sticky notes): AI Agent Node: System Prompt** Customize the chart interpretation (default: auto-selects bar/line/pie/etc. based on data). Example: Change to prioritize "scatter plots for correlations" if needed. Switch Nodes (File Detection)** Add rules for new formats (e.g., .ods for OpenDocument) in the conditions for XLS/XLSX/CSV/Sheets/Drive. HTTP Request Node (QuickChart)** Tweak chart params like width=800&height=600 for size, or colors via ?chart=... for branding. Let User Know Upload Complete: Text** Adjust the confirmation message template for tone (e.g., add emojis or custom phrasing). Test with a sample Slack message: "Make a line graph from this sales CSV over months." Results appear instantly with links. Potential Customizations Add Chart Types**: Duplicate a chart branch (e.g., Line Graph) and integrate new QuickChart endpoints for scatter or funnel charts. Switch AI Provider**: Replace OpenAI with OpenRouter in the Chat Model node for alternative LLMs. Batch Size**: Edit the Loop Over Items node's batch for larger datasets (default handles small files efficiently). Error Handling**: Add IF nodes post-extraction to notify on invalid data. Considerations and Improvements Rate Limits**: QuickChart free tier suits testing; scale to paid for heavy use. OpenAI tokens add up for complex data. File Limits**: Supports up to ~10MB uploads; for larger, preprocess externally. Privacy**: Data passes through OpenAI—review for sensitive info. Enhancements**: Integrate image OCR for scanned tables, or export to PDF for reports. This workflow streamlines data viz without coding, perfect for teams analyzing trends on the fly. Questions? Drop a Slack message in your bot channel!

✍️ AI-Powered High-Quality Blog Automation Automate SEO-optimized blog creation, publishing, and internal linking — designed for Lovable.dev or seamless WordPress integration. Proven to boost impressions by +15% weekly on real websites. 📌 Example: AI Applications in Real Estate Playbook — fully generated, optimized, and published with this automation. 🚀 What This Workflow Does Bring Your Own Keywords** You provide the keyword list — the workflow applies a scoring formula to rank them by relevance and competition for maximum SEO impact. Keyword Scoring & Logging** Scores primary and secondary keywords, logs them in PostgreSQL, and prevents reuse until strategically relevant. Deep Research & Blog Planning** Uses Perplexity AI and other AI models to outline, plan, and enrich each article with authoritative external sources. SEO-Optimized Redaction** Writes multi-part, long-form blogs with integrated internal links (to your existing content) and external links to reputable sites. Image Generation & Selection** Creates or selects high-quality header and in-article images, optimized for your topic. Full Blog Infrastructure** Internal & external linking logic Blog card + metadata updates Sitemap updates & Google indexing submission Post logging for future reference Publishing Flexibility** Direct GitHub commits for Lovable.dev WordPress-ready export Optional Slack approval before publishing 🔗 Integrated Services PostgreSQL** – Keyword & content database Perplexity AI** – Research & planning OpenAI / OpenRouter Models** – Multi-part blog writing Lovable.dev / GitHub** – Direct publishing WordPress-ready JSON output** – Easy CMS import Slack** – Approval workflow before going live 💼 What You Get Detailed Setup Guide** Workflow Description** 📥 Perfect For Website owners wanting consistent SEO growth Agencies handling multiple client sites Marketers running content-heavy campaigns Lovable.dev or WordPress users who want hands-off publishing 💡 Why You’ll Love It This is more than a blog writer — it’s a complete content infrastructure. From keyword prioritization to publishing and indexing, it keeps your site growing in reach and authority while you focus on your business.

✉️ AI-Powered 3-Step B2B Pipeline — Lead Gen → Cold Outreach → AI Drafted Email Management Automate your outbound workflow from prospecting to AI-generated reply drafts in Gmail — all fully integrated with your database. Ideal for SDRs, founders, and growth agencies who want speed, personalization, and control without losing human review. 📌 Example: From a verified lead list to a qualified opportunity — fully tracked, classified, and pre-drafted with this automation. 🚀 What This Workflow Does Step 1 — Lead Generation Import leads from Apollo. Enrich and verify emails. Store results in Supabase/Postgres, Step 2 — Cold Outreach Send 3 step personalized sequences via Mailgun/SendGrid. Track delivery, opens, replies, and bounces directly in your DB. Step 3 — Email Management (AI Drafts) Process only incoming Gmail messages from contacts in your DB. Generate personalized reply drafts, label threads, update lead status, and alert your team via Slack/Telegram. 🔗 Integrated Services Database:** Supabase/Postgres Outbound Email:** Mailgun / SendGrid Inbound Email & Drafts:** Gmail (OAuth2) AI Models:** OpenAI / Anthropic (JSON mode for classification + drafting) Alerts:** Slack / Telegram (optional) 💼 What You Get Detailed setup guide Fully connected 3-step pipeline in n8n Ready-to-use database schema Pre-built AI prompts for intent classification and professional replies 📥 Perfect For SDR teams doing targeted outbound Agencies managing multiple client campaigns Founders building scalable outbound systems Sales ops needing a no-risk, review-first reply process 💡 Why You’ll Love It This isn’t just another outreach template — it’s a full sales engine. From verified leads to AI-drafted replies, every action is logged, tracked, and controlled for maximum conversions without sacrificing oversight.

This is the core AI agent used for queryverify.com. Don't trust complex AI-generated SQL queries without double-checking them in a safe environment. That's where queryverify comes in. It automatically creates a test environment with the necessary data, generates code for your task, runs it to double-check for correctness, and handles errors if necessary. If you enable auto-fixing, queryverify will detect and fix issues on its own. If not, it will ask for your permission before making changes during debugging. In the end, you get thoroughly verified code along with full details about the environment it ran in. Setup It is an embedded chat for the website, but you can pin input data and run it on your own n8n instance. Input data sessionId: uuid\_v4. Required to handle ongoing conversations and to create table names (used as a prefix). threadId: string | nullable. If aiProvider is openai, conversation history is managed on OpenAI’s side. This is not needed in the first request—it will start a new conversation. For ongoing conversations, you must provide this value. You can get it from the OpenAIMainBrain node output after the first run. If you want to start a new conversation, just leave it as null. apiKey: string. Your API key for the selected aiProvider. aiProvider: string. Currently supported values: openai, openrouter. model: string. The AI model key (e.g., gpt-4.1, o3-mini, or any supported model key from OpenRouter). autoErrorFixing: boolean. If true, it will automatically fix errors encountered when running code in the environment. If false, it will ask for your permission before attempting a fix. chatInput: string. The user's prompt or message. currentDbSchemaWithData: string. A JSON representation of the database schema with sample data. Used to inform the AI about the current database structure during an ongoing conversation. Please use the '[]' value in the first request. Example string for filled db structure : '{"users":[{"id":1,"name":"John Doe","email":"[email protected]"},{"id":2,"name":"Jane Smith","email":"[email protected]"}],"products":[{"product_id":101,"product_name":"Laptop","price":999.99}]}' Make sure to fill in your credentials: Your OpenAI or OpenRouter API key Access to a local PostgreSQL database for test execution You can view your generated tables using your preferred PostgreSQL GUI. We recommend DBeaver. Alternatively, you can activate the “Deactivated DB Visualization” nodes below. To use them, connect each to the most recent successful Set node and manually adjust the output. However, the easiest and most efficient method is to use a GUI. Workflow Explanation We store all input values in the localVariables node. Please use this node to get the necessary data. OpenAI has a built-in assistant that manages chat history on their side. For OpenRouter, we handle chat history locally. That’s why we use separate nodes like ifOpenAi and isOpenAi. Note that if logic can also be used inside nodes. The AutoErrorFixing loop will run only a limited number of times, as defined by the isMaxAutoErrorReached node. This prevents infinite loops. The Execute_AI_result node connects to the PostgreSQL test database used to execute queries. Guidance on customization This setup is built for PostgreSQL, but it can be adapted to any programming language, and the logic can be extended to any programming framework. To customize the logic for other programming languages: Change instruction parameter in localVariables node. Replace the Execute_AI_result PostgreSQL node with another executable node. For example, you can use the HTTP Request node. Update the GenerateErrorPrompt node's prompt parameter to generate code specific to your target language or framework. Any workflows built on top of this must credit the original author and be released under an open-source license.

Automated AI-Driven Competitor & Market Intelligence System Problem Solved:** Small and Medium-sized IT companies often struggle to stay ahead in a rapidly evolving market. Manually tracking competitor moves, pricing changes, product updates, and emerging market trends is time-consuming, inconsistent, and often too slow for agile sales strategies. This leads to missed sales opportunities, ineffective pitches, and a reactive rather than proactive market approach. Solution Overview:** This n8n workflow automates the continuous collection and AI-powered analysis of competitor data and market trends. By leveraging web scraping, RSS feeds, and advanced AI models, it transforms raw data into actionable insights for your sales and marketing teams. The system generates structured reports, notifies relevant stakeholders, and stores intelligence in your database, empowering your team with real-time, strategic information. For Whom:** This high-value workflow is perfect for: IT Solution Providers & SaaS Companies: To maintain a competitive edge and tailor sales pitches based on competitor weaknesses and market opportunities. Sales & Marketing Leaders: To gain comprehensive, automated market intelligence without extensive manual research. Product Development Teams: To identify market gaps and validate new feature development based on competitive landscapes and customer sentiment. Business Strategists: To inform strategic planning with data-driven insights into industry trends and competitive threats. How It Works (Scope of the Workflow) ⚙️ This system establishes a powerful, automated pipeline for market and competitor intelligence: Scheduled Data Collection: The workflow runs automatically at predefined intervals (e.g., weekly), initiating data retrieval from various online sources. Diverse Information Gathering: It pulls data from competitor websites (pricing, features, blogs via web scraping services), industry news and blogs (via RSS feeds), and potentially other sources. Intelligent Data Preparation: Collected data is aggregated, cleaned, and pre-processed using custom code to ensure it's in an optimal format for AI analysis, removing noise and extracting relevant text. AI-Powered Analysis: An advanced AI model (like OpenAI's GPT-4o) performs in-depth analysis on the cleaned data. It identifies competitor strengths, weaknesses, new offerings, pricing changes, customer sentiment from reviews, emerging market trends, and suggests specific opportunities and threats for your company. Automated Report Generation: The AI's structured insights are automatically populated into a professional Google Docs report using a predefined template, making the intelligence easily digestible for your team. Team Notification: Stakeholders (sales leads, marketing managers) receive automated notifications via Slack (or email), alerting them to the new report and key insights. Strategic Data Storage & Utilization: All analyzed insights are stored in a central database (e.g., PostgreSQL). This builds a historical record for long-term trend analysis and can optionally trigger sub-workflows to generate personalized sales talking points directly relevant to ongoing deals or specific prospects. Setup Steps 🛠️ (Building the Workflow) To implement this sophisticated workflow in your n8n instance, follow these detailed steps: Prepare Your Digital Assets & Accounts: Google Sheet (Optional, if using for CRM data): For simpler CRM, create a sheet with CompetitorName, LastAnalyzedDate, Strengths, Weaknesses, Opportunities, Threats, SalesTalkingPoints. API Keys & Credentials: OpenAI API Key: Essential for the AI analysis. Web Scraping Service API Key: For services like Apify, Crawlbase, or similar (e.g., Bright Data, ScraperAPI). Database Access: Credentials for your PostgreSQL/MySQL database. Ensure you've created necessary tables (competitor_profiles, market_trends) with appropriate columns. Google Docs Credential: To link n8n to your Google Drive for report generation. Create a template Google Doc with placeholders (e.g., {{competitorName}}, {{strengths}}). Slack Credential: For sending team notifications to specific channels. CRM API Key (Optional): If directly integrating with HubSpot, Salesforce, or custom CRM via API. Identify Data Sources for Intelligence: Compile a list of competitor website URLs you want to monitor (e.g., pricing pages, blog sections, news). Identify relevant online review platforms (e.g., G2, Capterra) for competitor products. Gather RSS Feed URLs from key industry news sources, tech blogs, and competitor's own blogs. Define keywords for general market trends or competitor mentions, if using tools that provide RSS feeds (like Google Alerts). Build the n8n Workflow (10 Key Nodes): Start a new workflow in n8n and add the following nodes, configuring their parameters and connections carefully: Cron (Scheduled Analysis Trigger): Set this to trigger daily or weekly at a specific time (e.g., Every Week, At Hour: 0, At Minute: 0). HTTP Request (Fetch Competitor Web Data): Configure this to call your chosen web scraping service's API. Set Method to POST, URL to the service's API endpoint, and build the JSON/Raw Body with the startUrls (competitor websites, review sites) for scraping, including your API Key in Authentication (e.g., Header Auth). RSS Feed (Fetch News & Blog RSS): Add the URLs of competitor blogs and industry news RSS feeds. Merge (Combine Data Sources): Connect inputs from both Fetch Competitor Web Data and Fetch News & Blog RSS. Use Merge By Position. Code (Pre-process Data for AI): Write JavaScript code to iterate through merged items, extract relevant text content, perform basic cleaning (e.g., HTML stripping), and limit text length for AI input. Output should be an array of objects with content, title, url, and source. OpenAI (AI Analysis & Competitor Insights): Select your OpenAI credential. Set Resource to Chat Completion and Model to gpt-4o. In Messages, create a System message defining AI's role and a User message containing the dynamic prompt (referencing {{ $json.map(item => ... ).join('\\n\\n') }} for content, title, url, source) and requesting a structured JSON output for analysis. Set Output to Raw Data. Google Docs (Generate Market Intelligence Report): Select your Google Docs credential. Set Operation to Create document from template. Provide your Template Document ID and map the Values from the parsed AI output (using JSON.parse($json.choices[0].message.content).PropertyName) to your template placeholders. Slack (Sales & Marketing Team Notification): Select your Slack credential. Set Chat ID to your team's Slack channel ID. Compose the Text message, referencing the report link ({{ $json.documentUrl }}) and key AI insights (e.g., {{ JSON.parse($json.choices[0].message.content).Competitor_Name }}). PostgreSQL (Store Insights to Database): Select your PostgreSQL credential. Set Operation to Execute Query. Write an INSERT ... ON CONFLICT DO UPDATE SQL query to store the AI insights into your competitor_profiles or market_trends table, mapping values from the parsed AI output. OpenAI (Generate Personalized Sales Talking Points - Optional Branch): This node can be part of the main workflow or a separate, manually triggered workflow. Configure it similarly to the main AI node, but with a prompt tailored to generate sales talking points based on a specific sales context and the stored insights. Final Testing & Activation: Run a Test: Before going live, manually trigger the workflow from the first node. Carefully review the data at each stage to ensure correct processing and output. Verify that reports are generated, notifications are sent, and data is stored correctly. Activate Workflow: Once testing is complete and successful, activate the workflow in n8n. This system will empower your IT company's sales team with invaluable, data-driven intelligence, enabling them to close more deals and stay ahead in the market.

How it works This workflow automatically processes bank statements from various formats and extracts structured transaction data with intelligent categorization using AI. Key Steps File Upload - Accepts bank statements via webhook upload (PDF, Excel, CSV formats). Smart Format Detection - Automatically routes files to appropriate processors (PDF text extraction or spreadsheet parsing). AI-Powered Extraction - Uses GPT-4 to extract account details, transactions, and balances from statement data. Data Processing & Categorization - Cleans, validates, and automatically categorizes transactions into expense categories. Database Storage - Saves processed data to PostgreSQL database for analysis and reporting. API Response - Returns structured summary with transaction counts, expense totals, and category breakdowns. Set up steps Setup time: 8-12 minutes Configure OpenAI credentials - Add your OpenAI API key for AI-powered data extraction. Set up PostgreSQL database - Connect your PostgreSQL database and create the required table structure. Configure webhook endpoint - The workflow provides a /upload-statement endpoint for file uploads. Customize transaction categories - Modify the AI prompt to include your preferred expense categories. Test the workflow - Upload a sample bank statement to verify the extraction and categorization process. Set up database table - Ensure your PostgreSQL database has a bank_statements table with appropriate columns. Features Multi-format support**: PDF, Excel, CSV bank statements AI-powered extraction**: GPT-4 extracts account details and transactions Automatic categorization**: Expenses categorized as groceries, dining, gas, shopping, utilities, healthcare, entertainment, income, fees, or other Data validation**: Cleans and validates transaction data with error handling Database storage**: PostgreSQL integration for data persistence API responses**: Clean JSON responses with transaction summaries and category breakdowns Smart routing**: Automatic format detection and appropriate processing paths

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. AI-Powered Cold Email Sequence Workflow for n8n Unlock advanced cold email automation and boost your outbound results with this robust n8n workflow, engineered for scale, personalization, and flexibility. Harness AI-driven email content, dynamic lead handling, and intelligent scheduling—without writing code. What’s Inside Intelligent Multi-Step Email Outreach Automate 3-step cold email sequences for every prospect—all fully personalized and contextually adapted through AI research and copy generation. Each contact receives tailored, timely emails designed to maximize engagement and reply rates. Automated Personalization at Scale For every new lead, the workflow: Researches company and role context** using AI Identifies key pain points** and crafts custom hooks Builds multi-language, well-formatted HTML emails** with a consistent, brand-aligned tone This produces authentic, individualized communication—far more effective than generic mail merges. Advanced Scheduling & Delivery Logic Smart scheduling**: Sends distributed across optimal days/hours (configurable for your market) Throttled delivery**: Drip batching and dynamic waits to preserve deliverability Automated follow-ups**: Gentle, contextual nudges at precise intervals if there’s no reply Lead Management & Expansion Seamless database integration**: Email history, logic, and lead data fully synchronized with your backend (Supabase support included) Integrated lead generation**: Suite includes a companion workflow for sourcing, deduplicating, and enriching leads using Apollo, GPT-4, and AI—feeds directly into your campaign pipeline Built for Reliability and Scale Resilient against errors and duplicate sends Multi-sender rotation** for reputation management Easily customizable scheduling, content, languages, and batch size Tracks all critical data fields, like send history and reply status Use Cases B2B Sales Development** Automated Candidate Outreach (Recruitment)** Newsletter or Event Drip Campaigns** Startup Go-to-Market Sequences** Agency Lead Generation** Template Highlights AI-Powered Personalization**: Cold emails crafted by GPT-4 and prompt engineering Omnichannel Scheduling**: Dynamic batching, throttling, sender rotation Works Out-of-the-Box**: Connects to Mailgun, OpenAI, Supabase—simply insert credentials and leads Companion Lead Gen Workflow: Includes Apollo–AI–database pipeline for continuous sourcing that you can acces **for free in my profile. Flexible & Modular**: Adapt language, schedule, templates, or trigger events as your strategy evolves Best Practice Features No PII or sensitive data embedded—safe for corporate teams Modular zones for sequence creation, delivery, and tracking—clarity and easy expansion Clearly-named nodes and logical flows, following n8n community standards Robust error handling for high deliverability and low maintenance Experience end-to-end intelligent email automation—powered by n8n, trusted integrations, and state-of-the-art AI. Both the cold outreach workflow and the lead generation template are included. Discover, engage, and convert—at scale.