n8n Workflow for Brand Monitoring – X Mentions + Gemini Visual Analysis + Telegram Alerts

Brand monitoring used to mean hiring someone to sit on Twitter all day, scrolling endlessly through mentions of your company. They'd catch the obvious stuff but miss the subtle conversations that could make or break your reputation. What about that viral image mocking your product? Or that sarcastic tweet with perfect context but zero brand mentions?

Today's social media moves too fast for manual monitoring. You need something smarter. Something that can read between the lines, analyze images, and understand context like a human would. This n8n workflow does exactly that, combining AI-powered analysis with automated alerts to create a brand monitoring system that actually keeps up.

Prerequisites: What You'll Need to Get Started

Before diving into the workflow, gather these essential API keys and accounts:

• Apify API Token: For scraping X (Twitter) data using the kaitoeasyapi Twitter scraper
• Google Gemini API Key: Powers the AI analysis for both text sentiment and image recognition
• Airtable API Token: Stores and manages your monitoring data with duplicate prevention
• Telegram Bot Token: Sends real-time alerts to your monitoring team
• n8n Instance: Either cloud or self-hosted to run your automation

The beauty of this setup is that most of these services offer generous free tiers. You can start monitoring your brand without breaking the bank.

Key Components: The Building Blocks

This workflow uses several specialized n8n nodes working in harmony:

• Schedule Trigger: Runs the monitoring every 4 hours automatically
• HTTP Request Node: Connects to Apify's Twitter scraping service
• Google Gemini Nodes: Analyzes text sentiment and processes images
• Airtable Nodes: Manages data storage and prevents duplicate processing
• Switch Node: Routes posts based on relevance scores
• Telegram Node: Sends instant alerts for high-priority mentions
• Code Nodes: Handles JSON parsing and data transformation

Step 1: Configure Your Brand Monitoring Parameters

Start by setting up your search criteria in the Config node. This workflow monitors Tesla, but you can adapt it for any brand:

Search terms: "(Tesla OR $TSLA OR Cybertruck OR Model Y OR FSD) -Nikola"

Notice the clever exclusion of "Nikola" to avoid mentions of the inventor Nikola Tesla. This kind of nuanced filtering separates amateur monitoring from professional-grade systems. Set your minimum engagement threshold (this example uses 10 favorites) and decide how many tweets to analyze per run (20 in this case).

The workflow targets English-language posts from the last 24 hours, focusing on content with actual engagement rather than spam or bot activity.

Step 2: Scrape and Filter Social Media Data

The HTTP Request node calls Apify's Twitter scraper with your configured parameters. But here's where it gets smart: not everything that comes back is worth analyzing.

The "If Tweet" filter eliminates ads, promoted content, and other non-organic posts. It checks three conditions: the content must be an actual tweet, match your specified language, and have empty card data (filtering out link previews and promotional content).

This filtering saves you money on API calls and ensures you're only analyzing genuine user-generated content about your brand.

Step 3: Prevent Duplicate Analysis with Smart Storage

Nothing wastes resources like analyzing the same post twice. The workflow checks each tweet against your Airtable database using the unique post ID. If it finds a match, it skips analysis and moves to the next item.

This duplicate prevention is crucial for long-running monitoring systems. Without it, you'd reprocess viral posts every time they appear in search results, burning through API quotas and flooding your alerts with repeats.

Step 4: Analyze Text and Images with AI Intelligence

Here's where the magic happens. The workflow doesn't just look for keyword matches; it understands context, sentiment, and visual content.

For posts with images, Google Gemini analyzes each photo individually, providing descriptions and sentiment analysis. The results get processed into a structured format that combines with text analysis.

The main AI agent then synthesizes everything: post text, image analysis, and brand context to produce a relevance score from 0 to 10, overall sentiment, and reasoning for its decisions.

"You are a Head Brand Strategist for Tesla. Your job is to synthesize the provided analyses of a social media post's text and visuals into a final, conclusive assessment."

This approach catches nuanced mentions that keyword-based tools miss entirely.

Step 5: Route and Alert Based on Relevance

Not every mention deserves immediate attention. The Switch node routes posts into three categories:

• High Relevance (8-10): Logged as "New" status and triggers immediate Telegram alerts
• Medium Relevance (4-7): Stored for weekly review without urgent alerts
• Low Relevance (0-3): Discarded to avoid noise

High-priority alerts get formatted with author information, sentiment analysis, relevance scores, and direct links to the original posts. Your team sees exactly what needs attention and why.

Step 6: Store Everything for Analysis and Reporting

Every analyzed post gets stored in Airtable with complete metadata: author details, post content, creation dates, sentiment analysis, relevance scores, and AI reasoning. This creates a searchable database of brand mentions over time.

The structured data enables trend analysis, sentiment tracking, and performance measurement of your monitoring system itself.

Benefits and Use Cases Beyond Basic Monitoring

This workflow transforms brand monitoring from reactive damage control into proactive intelligence gathering:

• Crisis Prevention: Catch negative sentiment before it spreads
• Influencer Identification: Find authentic advocates and potential partnerships
• Competitive Intelligence: Monitor competitor mentions alongside your own
• Product Feedback: Analyze visual content showing your products in use
• Campaign Tracking: Measure organic response to marketing initiatives

The AI analysis provides context that traditional monitoring tools miss. Instead of drowning in notifications, you get curated intelligence that actually helps your business decisions.

Scale Your Brand Intelligence

Modern brand monitoring needs to be smart, not just comprehensive. This n8n workflow shows how combining multiple AI services with intelligent routing creates a system that thinks before it alerts.

The result? Your team focuses on meaningful conversations instead of sifting through noise. You catch problems early, identify opportunities faster, and understand your brand's true social media presence.

Start with one brand and expand from there. The workflow architecture scales easily to monitor multiple brands, competitors, or entire industry conversations. In the age of AI-powered automation, your brand monitoring should be at least as smart as your customers' conversations about you.