Cogninest AI Develops a Platform to Track and Analyze Domain Specific News Trends in Real Time

​Cogninest AI Builds Scalable Platform for Domain Specific News Trends

April 21, 2025· Amol Walunj

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Client Overview

Our client aimed to create a subscription based system that delivers designed, domain specific news and trend insights to users interested in sectors like Cybersecurity, Healthcare, and Finance. The goal was to provide timely, relevant updates by aggregating content from various sources and distributing it through structured email communications.

Challenges

  1. Handling Disparate Feeds The system needed to ingest content from diverse sources, including Reddit and custom RSS feeds, each with unique data formats and reliability concerns. Developing a robust pipeline to manage missing fields, inconsistent tags, and potential downtimes was essential.
  2. Ensuring Data Consistency and Avoiding Duplication With overlapping themes across articles, it was crucial to prevent duplicate storage and classification. Implementing a mechanism to identify and eliminate duplicates at scale was necessary.
  3. Accurate Classification into Trends Assigning articles to appropriate trends posed a challenge, especially when dealing with ambiguous headlines or partial matches. A consistent and accurate classification method was vital to maintain relevance.
  4. Scalability for Growing Subscriber Base As the subscriber base expanded, the system needed to efficiently process large batches of articles and send emails en masse without compromising performance.
  5. Automating Email Delivery Subscribers expected timely and relevant updates, necessitating an automated mechanism to query the database, assemble domain focused insights, and send structured, HTML rich emails.

Solutions and Implementation

Data Ingestion and Parsing

  • Developed dedicated scrapers for RSS and Reddit feeds to collect entries per domain.
  • Implemented HTML tag cleanup and text normalization for consistent downstream processing.​

Hash-Based Deduplication

  • Computed unique hashes of article titles and summaries before database insertion.
  • Discarded entries with existing hashes to ensure uniqueness.​

Domain-Specific Tables and Models

  • Created PostgreSQL backed schemas for each domain to store article metadata and trend related information.​

Trend Classification Flow

  • Checked for existing trends before assigning new articles.
  • Utilized a language model to cluster articles into new or emerging trends when necessary.
  • Assigned concise summaries to identified trends, inherited by subsequent related articles.​

Feedback Loop for Trend Quality

  • Implemented tasks to refine or merge overlapping trends based on thematic similarities discovered across multiple articles.​

Email Subscription and Delivery

  • Managed subscriber preferences and statuses in a dedicated table.
  • Composed visually structured HTML emails per domain, listing fresh trends and recommended articles.
  • Orchestrated daily or periodic email sends using Celery tasks, dispatching messages asynchronously to active subscribers.​

Automated Scheduling and Maintenance

  • Scheduled routine tasks for feed scraping, classification, and email distribution.
  • Ran scripts post trend updates to ensure accurate trend tags and dates.

Enhancements and Innovations

  • Dynamic Trend Tagging Adjusted daily content to reflect the "hotness" or popularity of trends, recalculating tags to provide real time traction insights.​
  • Merged Trend Discovery Merged similar topics into single named trends to avoid knowledge base fragmentation and streamline subscriber updates.​
  • Rich, Branded Email Layouts Designed HTML templates featuring logos, icons, and quick actions, enhancing reader engagement and subscription management.

Conclusion

The developed application offers a comprehensive solution for delivering customized, domain based trend insights. By integrating feed ingestion, trend extraction, and automated email features, it ensures timely and relevant updates for subscribers. The architecture supports scalability, accommodating growth in domains and daily article volumes, with clear pathways for incorporating new feed sources and enhancing AI-driven classifications.

At Cogninest AI, we specialize in helping companies build cutting edge AI solutions. To explore how we can assist your business, feel free to reach out to us at team@cogninest.ai