- AI summary: a concise, machine-generated synopsis of the article’s key facts
- Full text: the complete article body for downstream LLM processing, embedding, or RAG
- Sentiment :
positive,negative, orneutralclassification at the article level - News score : relevance and quality score for signal triage
- News tags : structured primary and secondary tags (e.g.
"Equity Fund-Raising","Layoffs","IPO") - Industry classifications : industry names with primary flag
- Company mentions : resolved array of all companies named in the article, each with UUID, name, and website
- Named entities : people, locations, products, and events extracted from the article text
- Classification codes : IPTC, IAB, NAICS, and SIC codes for cross-referencing against external taxonomies
- Article metadata : flags for whether an article is a press release, opinion piece, or breaking news, plus the country/countries associated with the event
- Company monitoring : pass a company’s website domain to track all coverage for a specific business, filtered by sentiment or news type
- Industry intelligence : resolve an industry via the Industry Search API, then pass the resulting code to monitor deal flow, regulatory activity, or innovation signals across an entire sector
- Open-ended topic monitoring : pass a free-text
queryto track a theme, event, or narrative that spans multiple companies and industries, without needing to resolve it to a company or industry code first - Entity tracking : filter or post-process by
entity_person_list,entity_location_list,entity_product_list, orentity_event_listto follow specific people, places, products, or events across coverage - LLM pipeline enrichment : use
full_textas input to summarisation, classification, or embedding models, pre-filtered bytypeandsentiment
This API is synchronous. Results are returned directly in the HTTP response. No job submission or polling required.
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