From raw articles to structured intelligence — how the analysis pipeline works.
NarrativeRadar continuously monitors over 130 sources organized into 8 geopolitical information blocks: Western media, Russian media, Chinese media, Middle Eastern media, African media, official government channels, financial press, and regional outlets.
Sources are selected for geographic and editorial diversity, not ideological alignment. New sources are added based on reach, reliability, and block coverage gaps. RSS feeds are checked every 30 minutes.
Individual articles are not shown to readers. Instead, a clustering engine groups related articles about the same development into a single event. Clustering uses five layers of matching:
An event is created only when multiple sources report on the same development. Single-source reports are tracked but not promoted.
Not every event receives the same depth of analysis. The tier depends on source coverage:
For events with Deep Analysis, the system identifies 2-3 competing narratives — distinct interpretive frameworks promoted by different information blocks. Each narrative includes:
If only one narrative exists for an event, it is deprioritized — narrative divergence is what makes an event analytically interesting.
When narratives conflict, the system identifies the specific points of disagreement across four dimensions:
Analysis is generated using AI models applied to source articles. AI is used for summarization, narrative identification, entity extraction, and scenario generation. All AI output is constrained by editorial rules that enforce neutrality and layer separation.
AI does not decide what is true or important. It structures information according to predefined rules. Every event page discloses that analysis is AI-assisted and editorially supervised.
Take a sanctions package against Russian oil exports — the kind of multi-block event the system handles every week. Within hours, articles arrive from Reuters, AP, TASS, Xinhua, Al Jazeera and Bloomberg. The clustering engine groups them into a single event because they share entities (Russia, Brent, EU, US Treasury), keyword signatures, and embedding similarity above the 0.65 threshold.
The enrichment prompt is then run with the article cluster as input. The output is structured into the layers above:
Crucially, the same prompt is applied to every qualifying event. The rules don't change because the topic is sensitive — they apply to a Russian oil package, a Federal Reserve announcement, and an Argentine election the same way. That uniformity is the methodology.
Events progress through four phases as coverage evolves:
Analysis is refreshed automatically as new articles arrive. Events are re-analyzed and may be upgraded to a higher tier as more sources report on them.