AI agents and RAG
Ground your agent in today's news.
Fintech
Monitor companies, screen people and backtest on news tagged with people, topics and country relevance.
Turn business news into inputs your models and analysts can filter.
Trading apps, risk teams and quant researchers.
The same fields on each article, whichever outlet published it.
Objectives
0/3Show the news for each ticker on its page.
Check counterparties against the news, not just a list.
Backtest ideas on dated, tagged headlines.
Put filtered coverage next to the numbers.
One article
CNBCOne query per ticker, limited to outlets you trust.
Illustrative scenario
A brokerage runs one search per ticker for the company and its CEO, limited to business news. The people field shows who is actually in each story.
At a glance
curl -G https://api.newsmesh.co/v1/search \ -d apiKey=$NEWSMESH_API_KEY \ --data-urlencode 'q=AMD OR "Lisa Su"' \ -d category=business \ -d sources=cnbc.com,bloomberg.com \ -d limit=10
import os, requestsr = requests.get( "https://api.newsmesh.co/v1/search", params={ "apiKey": os.environ["NEWSMESH_API_KEY"], "q": 'AMD OR "Lisa Su"', "category": "business", "sources": "cnbc.com,bloomberg.com", "limit": "10", },)articles = r.json()["data"]
const params = new URLSearchParams({ apiKey: process.env.NEWSMESH_API_KEY, q: 'AMD OR "Lisa Su"', category: "business", sources: "cnbc.com,bloomberg.com", limit: "10",});const res = await fetch(`https://api.newsmesh.co/v1/search?${params}`);const { data } = await res.json();
{
"data": [{
"article_id": "7997c2d7b2f98bd95b01a3882f00fc64",
"title": "AMD briefly joined the $1 trillion club. Cramer says its mo…",
"source": "CNBC",
"category": "business",
"topics": ["AI trends", "CPU market", "business strategy"],
"people": ["Jim Cramer"],
"country_relevance": ["United States"],
"link": "https://www.cnbc.com/2026/09/28/jim-cramer-amd-lisa…"
}],
"next_cursor": "eyJwIjoi…"
}