PressPulse AI

Automatically identifies and filters relevant media coverage opportunities by analyzing journalist beats, publication focus areas, and company/product relevance using NLP-based matching against a continuously updated media database. The system likely employs semantic similarity scoring between company profiles and journalist coverage patterns to surface high-intent leads rather than generic press lists.

Implements a scheduled batch processing pipeline that aggregates newly discovered media leads, ranks them by relevance, and delivers a curated digest email every morning at a consistent time. The system maintains user preferences for digest frequency, content depth, and filtering criteria, then orchestrates email delivery through a transactional email service.

Maintains and continuously updates detailed profiles for journalists including beat coverage, recent articles, publication history, social media presence, and contact information. The system likely crawls publication websites, monitors journalist social accounts, and aggregates data from multiple sources to create a comprehensive profile that enables relevance matching and outreach personalization.

Implements a machine learning-based ranking system that scores journalist leads based on semantic similarity between company profile and journalist beat coverage, publication tier, recent activity, and other contextual factors. The algorithm likely uses embeddings-based retrieval or collaborative filtering to surface the most relevant journalists first, with scores visible in the digest to help users prioritize outreach.

Maintains a continuously updated database of journalists, publications, and coverage topics through automated web scraping, publication RSS feeds, social media monitoring, and data partnerships. The system crawls publication websites to extract journalist bylines, monitors beat assignments, tracks job changes, and updates contact information to keep the database current and accurate.