AI with real-time context, automated signals
Real-time news API and web content data — structured and enriched by AI, primed for LLMs.
Perigon's Stories endpoint clusters related articles by event or topic, auto-updates as stories evolve, and provides AI-generated summaries plus key points. This lets users pinpoint major narratives and measure coverage scope and reach without manually aggregating articles.
Perigon tags every article with AI-powered labels (bias, opinion, paywalls, synthetic content), sentiment scores (positive/negative/neutral), topics, categories, and medium type. Users can include or exclude by any combination to surface only high-quality, relevant content and block sponsored or low-value pieces.
Perigon's Articles endpoint ingests millions of news stories daily from 180,000+ global sources and allows filtering by company name, ticker, person name, Wikidata ID, or Boolean/vector search queries. Users can track exact mentions, exclude noise, and combine synonyms to catch all relevant coverage without manual monitoring.
Perigon's entity detection and knowledge graph mapping identifies real-world entities (people, companies, locations), resolves them across articles and sources, and traces relationships. Users can filter by entity to see how different actors connect across stories and time.
Perigon's vector search endpoint accepts plain-English prompts (e.g., 'Which AI startups raised Series A funding this week?'), returns semantically similar articles with relevance scores, and integrates via Model Context Protocol (MCP) into Claude, Cursor, or any MCP-compatible client. No complex Boolean syntax required.