Proof of Concept: RewardsRadar
How an autonomous media outlet uses the RadarCore engine to extract high-confidence signals from thousands of sources without human intervention.
The Challenge
RewardsRadar is an autonomous media platform focused on the gaming industry. Their goal is to monitor hundreds of game studios, RSS feeds, subreddits, and official websites to detect new updates, patch notes, leaks, and promotional codes the second they happen. Manual curation was impossible at this scale.
The Solution: Powered by RadarCore
RewardsRadar became the first internal customer of the RadarCore intelligence engine. By offloading the ingestion complexity to the RadarCore API, RewardsRadar transformed into a simple UI layer that consumes pristine JSON insights.
Continuous Monitoring
RadarCore monitors thousands of configured sources. It automatically detects state changes and fetches the raw HTML/XML content.
Signal Extraction
LLMs structurally parse the noise into "Signals" (facts, rumors, patch notes) mapped to strict JSON schemas.
Trend Clustering
Signals from disparate sources are merged into Insight Clusters, preventing duplicate news and increasing confidence scores.
Editorial Generation
High-confidence clusters automatically trigger editorial workflows, producing ready-to-publish news and guides.
The Results
- Zero Manual Curation: Over 10,000 signals extracted monthly with complete autonomy.
- High Resilience: RadarCore's automated model-fallback chain ensures API outages never bring down the ingestion pipeline.
- Instant Publishing: News hits the site within 15 minutes of the original source change.