The client is a global media monitoring firm that provides real-time news insights to corporate executives. They handle thousands of news sources and social feeds, requiring instant analysis and summarization.

They needed an automated way to filter noise and deliver high-quality, relevant reports to their subscribers without manual intervention.

Managing massive data streams was proving impossible for a human team:

• High volume of duplicate or irrelevant news stories across different regions.

• Difficulty in categorizing complex topics like emerging tech or geopolitical shifts accurately.

• Scaling report generation to 24/7 coverage without hiring dozens of analysts.

• Integrating multiple third-party APIs into a cohesive, reliable workflow.

Our objectives centered on speed and intelligence:

• Deploy an autonomous pipeline to aggregate news from 500+ sources.

• Use AI to rank and score news relevance based on client-specific keywords.

• Automate the generation of daily executive briefs via Slack and Email.

• Ensure the system can recover from API rate limits or downtime automatically.

We designed an n8n-based automation framework that acts as the central nervous system for their data intake. Using custom Node.js hooks, we connected scraping tools to OpenAI's GPT-4 for deep text analysis.

The system uses a vector-based filtering approach to ensure that only unique, high-value stories reach the final reporting stage.

We built a multi-layered AI agent workflow:

Key Features

• Automated ingestion from RSS, Webhooks, and Scraping APIs.

• Topic modeling and sentiment analysis using Large Language Models.

• Custom report templates that auto-populate based on filtered content.

• A feedback loop where users can 'upvote' stories to improve AI accuracy.

• Automation Architect: Designed the n8n logic and error-handling flows.

• AI Engineer: Configured LLM prompts and fine-tuning for news classification.

• Backend Developer: Built custom API integrations for legacy data sources.