- We collect the most current content from a wide variety of sources, including blog posts, shared links from social media and literally thousands of news sites around the world.
- Each incoming news article is semantically analyzed and matched with a distinct topic as defined in our unique updt.me Topic Ontology. This process filters out unrelated articles that would otherwise be included in a simple keyword match.
- A news article may be related to a topic but not particularly relevant. We score each news item based on a variety of factors, including matching related semantic entites, link clicks and social buzz.
- To avoid flooding topic subscribers with redundant articles, we carefully deduplicate news on the same "news story" and only publish most recent developments or articles which are significantly different in their content.
- Our engine can publish to a wide variety of target channels: Email, Social Media (Facebook Pages, Twitter), Mobile Apps, Websites, RSS Feeds and Email. Your feed will be 100% on-topic and relevant for your channel’s subscribers.
- We consistently track user interaction with the content throughout the process. We allow you to tweak the weightings of “buzz” factors in the scoring process (e.g. minimum number of times a news item was shared) and provide insightful end-to-end metrics.