The Structured Data Extract & Data Mining workflow is crafted for researchers, content analysts, SEO strategists, and AI developers who need to transform semi-structured web data (like markdown content or scraped HTML) into actionable structured datasets.
It is ideal for:
Content Analysts - Organizing and mining large volumes of markdown or HTML content.
SEO & Trend Researchers - Exploring topics by location and category.
AI Engineers & NLP Developers - Looking to automate insight extraction from unstructured inputs.
Growth Marketers - Tracking topic-level trends for strategic campaigns.
Automation Specialists - Streamlining workflows from scrape to storage.
Extracting insights from markdown or HTML documents typically requires manual review, formatting, and parsing. This becomes unscalable when dealing with large datasets or when real-time response is needed. Additionally, trend and topic extraction usually involves external tools, custom scripts, and inconsistent formatting.
This workflow solves:
Automatic text extraction from markdown or structured content.
Location and category-based trend mining with semantic grouping.
AI-driven topic extraction and summarization
Real-time notification via webhook with rich structured payloads.
Persistent storage of mined data to disk for audits or further processing.
Receives input: Sets the URL for the data extraction and analysis.
Uses Bright Data's Web Unlocker to extract content from relevant sites.
A Markdown/Text Extractor node parses the content into clean plaintext
The cleaned data is passed to Google Gemini to:
Identify trends by location and category
Extract key topics and themes
Format the response into structured JSON
The structured insights are sent via Webhook Notification to external systems (e.g., Slack, Web apps, Zapier)
The final output is saved to disk
Update Source : Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics.
Gemini Prompt Customization :
Extract trends within a custom category (e.g., E-commerce design patterns in the US)
Output topics with popularity metrics
Structure the output as per your database schema (e.g., [{ topic, trend_score, location }])
Webhook Output : Send notifications to -
Slack – with AI summaries in rich blocks
Internal APIs – for use in dashboards
Zapier/Make – for multi-step automation
Persistence
Save output to: