About the Built with n8n category

This is a category for showing off the projects that you’ve built with n8n.

Everyone is invited to ask questions to the authors of the topics about their projects.

Examples of posts that can be shared here include processes that you have automated using n8n workflows and apps or products that you’ve built that are powered by n8n.

Note: A better place to showcase single workflow projects is the workflows page.

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Hi Tanay, I am looking to build an appointment AI for callback and

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اريد ان انشاء قاعدت بينات لتطبيق محسبي متكامل

Hello!

I am a new n8n user with a strong eagerness to learn. I am an IT graduate, and while professional responsibilities have kept me working with more traditional technologies, I am now intentionally refocusing my efforts on AI and automation to upgrade my skill set and pursue new career opportunities. I look forward to learning from this community and contributing as I continue to grow professionally. This is what im tryin to build…

Chat-Based Order Processing Workflow (Concept Overview)

I am designing a chat-based order automation workflow using n8n and AI with the following objectives:

  1. Customer Interaction Channel
    Customers initiate orders by sending messages to my Facebook Page Messenger.

  2. AI-Powered Message Processing
    Incoming messages are received by the workflow and processed by an AI component that:

    • Interprets unstructured chat messages

    • Extracts structured order information

  3. Multilingual Support (Waray-Waray to English)
    Since most customers will not use English, the AI will:

    • Detect messages written in Waray-Waray (Philippine dialect)

    • Translate the content into English prior to processing

    • Use the translated text as the basis for order understanding and extraction

  4. Order Data Extraction Requirements
    The AI should extract and normalize the following details:

    • Ordered Items

      • Item name

      • Quantity

    • Order Source

      • Vendor or store name

      • Support for multiple items from multiple sources within a single message

    • Delivery Location

      • Barangay or specified delivery area

    Example Input (Customer Message):
    “Chicken 1pc from Jollibee and burger plain 2pcs from McDonald’s, deliver to Barangay E.”

    Expected Structured Output:

    • Ordered Item: Chicken, Quantity: 1 pc, Source: Jollibee

    • Ordered Item: Burger (Plain), Quantity: 2 pcs, Source: McDonald’s

    • Delivery Location: Barangay E

  5. Workflow Goal (Initial Phase)
    The primary goal of this first iteration is to reliably transform free-text chat messages into clean, structured order data that can later be:

    • Stored in a database or Google Sheets

    • Used for order confirmation messages

    • Integrated with downstream fulfillment or delivery workflows

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مرحباً @safwt771754091s
سيد صفوت
هل انجزت المشروع الذي تحدثت عنه حول إنشاء برنامج يستند إلى قاعدة البيانات،

I have built so many project with the help of n8n like Voice Assistant Agent that controls my social Media and Google account . If anyone wants to work with me . Contact with the page https://portfolio-ai-assistant-of-malik.vercel.app/

Building a Local-First Automation Portfolio with n8n

Over the last weeks I’ve been building a local-first automation environment around n8n running on Apple Silicon.

The goal was not to build isolated workflows, but to explore how far a self-hosted setup could go when treated as an internal automation platform rather than a simple workflow editor.

The stack currently runs locally using:

  • n8n
  • PostgreSQL
  • Docker + Colima
  • External-disk backed persistent storage
  • Automated backups and validation scripts
  • Secret scanning and release packaging

The project currently consists of three workflow case studies:

1. Job Application Intelligence Pipeline

A workflow for importing, validating, enriching and persisting job application data into PostgreSQL with import auditing and reporting.

2. Fleet Operations Automation System

A workflow exploring operational visibility, vehicle tracking and fleet-related processes inspired by real operational environments.

3. Local AI Knowledge Processing Workflow

A local-first document ingestion and knowledge processing pipeline focused on privacy and self-hosted operation.

Some of the more interesting challenges were not the workflows themselves but the surrounding operational details:

  • PostgreSQL persistence verification
  • File access restrictions
  • Container path visibility
  • Import/export packaging
  • Backup validation
  • Secret sanitization for public releases
  • Reproducible local deployments

One issue that took some investigation was file access restrictions inside n8n containers and ensuring workflows could only access explicitly allowed paths through N8N_RESTRICT_FILE_ACCESS_TO while still maintaining a usable import process.

The entire project is now published publicly:

GitHub:

Live presentation:

I also recently joined the community under the username kostasuser01gr and started participating in discussions around PostgreSQL, AI-assisted queries and workflow troubleshooting.

I’d be interested to hear how others approach local-first deployments, workflow packaging and release management around n8n projects.