🛡️ BrandGuard AI

Intelligent Brand Reputation Monitoring System
Automated AI-powered brand monitoring with automated analysis, reporting, and a natural language Slack interface.

BrandGuard AI is a comprehensive, fully automated brand reputation monitoring system built on n8n. It is designed to:

• 🕷️ Collect brand mentions from major social platforms like Twitter, Reddit, and LinkedIn.
• 🧠 Analyze each mention for sentiment, urgency, and business impact using Google Gemini AI.
• 🗄️ Store enriched data in a structured MongoDB database.
• 📊 Generate automated daily and weekly analytical reports with strategic insights.
• 🖥️ Visualize data through a dynamic web dashboard.
• 💬 Interact with users via a smart Slack bot that understands natural language queries.
• 🚨 Alerts your team about critical mentions.

The system is a collection of interconnected n8n workflows that create a full data processing pipeline, from collection to user-facing interfaces.

graph TD
    subgraph "Data Collection (BrightData)"
        A[Twitter]
        B[Reddit]
        C[LinkedIn]
    end

    subgraph "Processing & Enrichment (n8n + Google AI)"
        D(Step 1: Create Jobs) --> E(Step 2: Normalize Data)
        E --> F(Step 3: AI Analysis)
    end

    subgraph "Data Storage"
        G[(MongoDB)]
    end

    subgraph "Outputs & Interfaces"
        H{Reports}
        I[Web Dashboard]
        J[AI Slack Bot]
    end

    A & B & C --> D
    F --> G

    G --> H
    G --> I
    G --> J

The system's logic is orchestrated by a series of modular n8n workflows:

• Brand Monitoring Step 1: Schedules data collection from BrightData (Twitter, Reddit, etc.) and creates processing jobs in MongoDB.
• Brand Monitoring Step 2: Fetches raw data for pending jobs, normalizes it into a standard format, and triggers the AI analysis step.
• Brand Monitoring Step 3: The core AI enrichment pipeline. It takes normalized data, uses Google Gemini to perform a deep analysis (sentiment, urgency, topics), and saves the structured result to the main mentions collection in MongoDB.

• AI Daily Summary: Runs daily to analyze all mentions of the previous day, generates a detailed executive summary for each brand, and saves it to the day_reports collection.
• AI Weekly Summary: Runs weekly, aggregating daily reports to produce a high-level strategic summary with trend analysis. Saved to the weekly_reports collection.

• BrandGuard AI - Dashboard: A webhook-powered dashboard that runs complex MongoDB aggregation queries to visualize analytics like mention volume, sentiment breakdown, and top sources using Chart.js.
• Brand Mention Report: Serves a paginated, searchable web view of all mentions stored in the database.
• Daily Summary View & Weekly Summary View: Provide web interfaces to browse and view the generated AI reports.
• API Detailed Post: A simple API endpoint to fetch the full data for a single mention, used by the front-end.

• BrandGuard AI bot: A sophisticated, AI-powered Slack bot that uses Google Gemini for Natural Language Understanding (NLU). Users can ask complex questions in plain English to get data from the system.
• Error Handler: A global error handler that catches any workflow failures and reports them in Slack for immediate attention.

Forget rigid slash commands. The BrandGuard AI bot understands natural language. You can simply chat with it in a direct message.

Examples of what you can ask:

• "Show me what's up with Apple today"
• "Are there any critical mentions?"
• "Find mentions about battery life"
• "Give me the daily summary"
• "What's new with Samsung?"
• "Show the last 5 critical mentions"
• "Search for mentions about product quality"

The bot uses Google Gemini to parse your intent and parameters, queries the database, and delivers a formatted, easy-to-read response right in Slack.

• Docker and Docker Compose
• An active n8n instance (can be run via the provided docker-compose.yml)
• MongoDB database (included in docker-compose.yml)
• Google AI API credentials
• BrightData API credentials
• Slack workspace with permissions to create an App

1. Set up:

   • Clone the repository.

   git clone https://github.com/premananda108/BrandGuardAI.git
   cd BrandGuardAI

2. Launch Services:

   • Run docker-compose up -d. This will start three containers: n8n, mongodb, and mongo-express.
   • Access n8n at http://localhost:5678.
   • Access Mongo Express (a database GUI) at http://localhost:8081 (user="admin", password="supersecret") and create database "brandguard"

3. Configure n8n:

   • Install BrightData node: Find and Install BrightData node.

   • Set up Credentials: In the n8n settings (Settings -> Credentials), add your API keys for Google AI, BrightData and Slack.

   • Set up Credentials MongoDB: In the n8n settings (Settings -> Credentials), select MongoDb (Connection String = "mongodb://mongodb:27017", Database = "brandguard").

   • Import Workflows: Import all .json files from the /workflows directory into n8n.

   • Activate Workflows: Activate all the imported workflows. The system is now live.

• Workflow Engine: n8n
• Database: MongoDB
• AI Processing: Google Gemini AI
• Data Collection: BrightData
• Web Interface: HTML/CSS/JavaScript (served directly from n8n)
• Team Integration: Slack
• Visualization: Chart.js

For more detailed information, please refer to the markdown documents in the repository, which include:

• MONGODB_SETUP_NOTES.md: Notes on setting up and using MongoDB.
• SLACK_CONNECTION_GUIDE.md: Guide for setting up the Slack integration.
• And many more covering specific features and setups.

This project is licensed under the terms of the LICENSE file.

Built with ❤️ by Premananda

Protecting your brand reputation, one mention at a time.