> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wokelo.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Company News Monitoring
> Fetch the latest news articles for any company, enriched with AI-generated summaries, event categories, sentiment signals, publisher metadata, and full article text — returned synchronously with flexible date, category, and publisher filtering.
## 1. Overview
The Company News Monitoring API fetches the latest news articles for a given company, enriched with AI-generated summaries, structured event categories, sentiment classifications, publisher metadata, and scraped article text. Results are returned synchronously in the HTTP response — no job polling required.
This is a **synchronous GET API** — you pass a company identifier and optional filters as URL parameters, and receive a paginated array of enriched news articles immediately.
Each article in the response includes:
* **AI summary** — a concise, machine-generated synopsis of the article's key facts and implications
* **Event categorisation** — a `primary_tag` and up to several `secondary_tags` drawn from Wokelo's 86-category news taxonomy
* **Sentiment signal** — `Positive`, `Neutral`, or `Negative` classification at the article level
* **Newsworthiness impact** — `High`, `Medium`, or `Low` rating assessing the article's strategic significance
* **Publisher and author metadata** — source domain, author name, publication date, and original language
* **Company mentions** — a structured array of all companies named in the article, with their resolved websites
* **Full article text** — the complete `scraped_text` for downstream summarisation, embedding, or LLM processing
**Common use cases:**
* **Portfolio adverse signal monitoring** — Filter for `LEGAL AND COMPLIANCE`, `LAY-OFFS`, `FINANCIAL DISTRESS & RESTRUCTURING`, or `CYBERSECURITY INCIDENTS` categories to catch early warning signals across a watchlist
* **Deal due diligence** — Pull all news for a target company in the past 12 months, filter by `MERGERS & ACQUISITIONS`, `REGULATORY APPROVALS & CERTIFICATIONS`, and `LEGAL PROCEEDINGS & LITIGATION` to surface relevant deal context fast
* **Competitive intelligence** — Monitor `PRODUCT LAUNCHES & ENHANCEMENTS`, `PARTNERSHIPS & ALLIANCES`, and `GEOGRAPHIC EXPANSION` categories for a set of competitors on a rolling basis
* **Executive monitoring** — Track `C-SUITE APPOINTMENTS & DEPARTURES`, `EXECUTIVE COMMENTARY & INTERVIEWS`, and `SUCCESSION PLANNING` for key personnel across your coverage universe
* **LLM-powered research** — Use the `scraped_text` field to feed full article content directly into your own summarisation or RAG pipeline, pre-filtered by category and date
This API is synchronous. Results are returned directly in the HTTP response — no job submission or polling required. See [How Sync APIs work](/how-sync-apis-work).
***
## 2. Quick Start
**Step 1 — Make a simple request**
```bash cURL theme={"system"}
curl --location 'https://api.wokelo.ai/api/enterprise/company/news/?company=tesla' \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json'
```
```python Python theme={"system"}
import requests
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json"
},
params={
"company": "tesla"
}
)
data = response.json()
print(f"Retrieved {data['count']} articles")
```
**Step 2 — Filter by category and date range**
```python theme={"system"}
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers={"Authorization": "Bearer "},
params={
"company": "tesla",
"category": "MERGERS & ACQUISITIONS, PARTNERSHIPS & ALLIANCES",
"start_date": "2025-01-01",
"end_date": "2026-03-31",
"limit": 50
}
)
articles = response.json()["data"]
```
**Step 3 — Work with the articles**
```python theme={"system"}
for article in articles:
print(f"[{article['primary_tag']}] {article['title']}")
print(f" {article['publisher']} | {article['published_date'][:10]} | {article.get('sentiment', 'N/A')}")
print(f" {article['ai_summary'][:120]}...")
print()
```
***
## 3. Authentication
All requests must include a **Bearer token** in the `Authorization` HTTP header. No other authentication method is supported.
```text theme={"system"}
Authorization: Bearer
```
API tokens are issued from your Wokelo account. Navigate to **Account Details → API Credentials** in the Wokelo dashboard to get your client id and client secret. Contact [support@wokelo.ai](mailto:support@wokelo.ai) if you do not yet have API access.
Never expose your token in client-side code, browser requests, or public repositories. A missing or invalid token returns `401 Unauthorized`. A valid token without sufficient plan permissions returns `403 Forbidden`.
***
## 4. Request Reference
**Endpoint**
```text theme={"system"}
GET https://api.wokelo.ai/api/enterprise/company/news/
```
All parameters are passed as URL query parameters.
| Parameter | Type | Required | Description |
| ------------- | ------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `company` | string | **Required** | Company permalink (e.g. `"tesla"`) or a full company URL (e.g. `"https://www.tesla.com/"`). Use the [Company Search API](/supporting-apis-doc) to resolve a company name to its permalink. |
| `start_date` | string | Optional | Start of the date window for articles (`YYYY-MM-DD`). Inclusive. |
| `end_date` | string | Optional | End of the date window for articles (`YYYY-MM-DD`). Inclusive. |
| `category` | string | Optional | Comma-separated list of news categories to filter by. Returns only articles whose `primary_tag` or `secondary_tags` match. [View all 86 supported categories](/news-categories). Example: `"MERGERS & ACQUISITIONS, PRODUCT LAUNCHES & ENHANCEMENTS"`. |
| `limit` | integer | Optional | Maximum number of articles to return. Default `100`, max `1000`. |
| `offset` | integer | Optional | Number of articles to skip before returning results. Use with `limit` for pagination. |
| `blacklisted` | string | Optional | Comma-separated list of publisher domains to exclude. Include the full domain starting with `https://` (e.g. `"https://www.prnewswire.com/"`). |
| `publishers` | string | Optional | Comma-separated list of publisher domains to include exclusively. All other publishers are excluded. |
**Full request example:**
```bash cURL theme={"system"}
curl --location 'https://api.wokelo.ai/api/enterprise/company/news/?company=tesla&start_date=2025-01-01&end_date=2026-03-15&category=PARTNERSHIPS%20%26%20ALLIANCES%2C%20MERGERS%20%26%20ACQUISITIONS&limit=100&offset=0&blacklisted=https%3A%2F%2Fwww.prnewswire.com%2F' \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json'
```
```python Python theme={"system"}
import requests
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json"
},
params={
"company": "tesla",
"start_date": "2025-01-01",
"end_date": "2026-03-15",
"category": "PARTNERSHIPS & ALLIANCES, MERGERS & ACQUISITIONS",
"limit": 100,
"offset": 0,
"blacklisted": "https://www.prnewswire.com/"
}
)
print(response.json())
```
***
## 5. Response
### Response structure
```json theme={"system"}
{
"status": "success",
"data": [ ...article objects... ],
"count": 4,
"total": 4,
"limit": 100,
"offset": 0
}
```
| Field | Type | Description |
| -------- | ------- | -------------------------------------------------------------------------------------- |
| `status` | string | `"success"` when the request was processed successfully. |
| `data` | array | Array of enriched article objects. |
| `count` | integer | Number of articles returned in this response. |
| `total` | integer | Total number of matching articles available (before `limit` and `offset` are applied). |
| `limit` | integer | The `limit` value that was applied. |
| `offset` | integer | The `offset` value that was applied. |
### Article object fields
Each object in the `data` array contains the following fields:
**Article content**
| Field | Type | Description |
| ------------------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `title` | string | Headline of the article. |
| `url` | string | Canonical URL of the original article. |
| `ai_summary` | string | AI-generated 2–4 sentence summary of the article's key facts and implications. |
| `scraped_text` | string | Full text content of the article as scraped from the source. Use this for downstream LLM processing, embeddings, or detailed analysis. May be empty for paywalled articles. |
| `published_date` | string | Publication datetime in `YYYY-MM-DD HH:MM:SS` format (UTC). |
| `original_language` | string | ISO 639-1 language code of the original article (e.g. `"EN"`). Empty string when not detected. |
**Publisher metadata**
| Field | Type | Description |
| ----------- | ------ | --------------------------------------------------------------------------------------------------------------------- |
| `publisher` | string | Domain name of the publishing outlet (e.g. `"bloomberg"`, `"techcrunch"`). |
| `author` | string | Author name(s) as extracted from the article. May contain multiple names or be empty. |
| `source` | string | Wokelo's internal signal source designation. Either `"Wokelo"` (directly indexed) or `"Google"` (sourced via search). |
**Categorisation**
| Field | Type | Description |
| ---------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `primary_tag` | string | The single most relevant event category from Wokelo's 86-category taxonomy. See [supported categories](/news-categories). May be empty for older articles. |
| `type` | string | Alias for `primary_tag`. Present in some responses; use `primary_tag` as the canonical field. |
| `secondary_tags` | string\[] | Additional relevant categories from the taxonomy. Empty array when no secondary categories apply. |
**Signals**
| Field | Type | Description |
| ----------------------- | ------ | ---------------------------------------------------------------------------------------------------------- |
| `sentiment` | string | Article-level sentiment: `"Positive"`, `"Neutral"`, or `"Negative"`. Empty string when not yet classified. |
| `newsworthiness_impact` | string | Strategic significance rating: `"High"`, `"Medium"`, or `"Low"`. Empty string when not yet classified. |
**Geography & company mentions**
| Field | Type | Description |
| --------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `countries` | string\[] | Array of ISO 3166-1 alpha-3 country codes relevant to the article (e.g. `["USA", "CHN"]`). Empty array when not detected. |
| `company_name` | string | The company that was queried (mirrors the `company` parameter). |
| `company_names` | object\[] | Structured array of all companies mentioned in the article. Each object contains `name` (string) and `website` (string). Useful for identifying deal counterparties, partners, and competitors referenced in the article. |
The `company_names` array includes all entities mentioned in the article — not just the queried company. This makes it useful for surfacing deal counterparties, lead investors, named competitors, and co-signatories in regulatory filings without having to parse the article text.
### Sentiment values
| Value | Meaning |
| ------------ | --------------------------------------------------------------------------------------------- |
| `"Positive"` | Article conveys favourable news — growth, wins, new partnerships, positive earnings |
| `"Neutral"` | Factual or mixed coverage with no clear directional bias |
| `"Negative"` | Article conveys unfavourable news — regulatory action, layoffs, declining metrics, litigation |
| `""` | Sentiment classification not yet available for this article |
### Newsworthiness impact values
| Value | Meaning |
| ---------- | ------------------------------------------------------------------------------------------- |
| `"High"` | Material signal likely to affect strategic positioning, valuation, or stakeholder decisions |
| `"Medium"` | Notable development worth tracking but not immediately actionable |
| `"Low"` | Routine coverage, minor updates, or tangentially related content |
| `""` | Impact rating not yet available for this article |
### Notes on empty fields
Several fields can be empty strings or empty arrays in valid responses:
* `sentiment`, `newsworthiness_impact`, `primary_tag`, `original_language` — empty string when classification has not yet been applied to this article, particularly for older content
* `secondary_tags`, `countries`, `company_names` — empty array when no values were detected
* `scraped_text` — empty string for paywalled articles or pages that blocked scraping
* `author` — empty string when the byline was not present or parseable
***
## 6. Examples
### Portfolio adverse signal monitoring
Get all legal, compliance, layoff, and financial distress news for a portfolio company to catch early warning signals.
```bash cURL theme={"system"}
curl --location 'https://api.wokelo.ai/api/enterprise/company/news/?company=brex&category=LEGAL%20AND%20COMPLIANCE%2C%20LAY-OFFS%2C%20EXECUTIVE%20APPOINTMENT%20AND%20CHANGES%2C%20REGULATORY%20%26%20COMPLIANCE%2C%20FINANCIAL%20DISTRESS%20%26%20RESTRUCTURING%2C%20WORKFORCE%20REDUCTIONS' \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json'
```
```python Python theme={"system"}
import requests
HEADERS = {"Authorization": "Bearer "}
ADVERSE_CATEGORIES = ", ".join([
"LEGAL PROCEEDINGS & LITIGATION",
"REGULATORY APPROVALS & CERTIFICATIONS",
"REGULATORY & COMPLIANCE",
"WORKFORCE REDUCTIONS",
"FINANCIAL DISTRESS & RESTRUCTURING",
"CYBERSECURITY INCIDENTS",
"REPUTATIONAL DAMAGE",
"C-SUITE APPOINTMENTS & DEPARTURES"
])
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=HEADERS,
params={
"company": "brex",
"category": ADVERSE_CATEGORIES,
"start_date": "2025-01-01",
"limit": 100
}
)
articles = response.json()["data"]
# Surface high-impact adverse signals first
high_impact = [a for a in articles if a.get("newsworthiness_impact") == "High"]
print(f"Found {len(high_impact)} high-impact adverse signals\n")
for article in sorted(high_impact, key=lambda x: x["published_date"], reverse=True):
print(f"[{article['primary_tag']}] {article['title']}")
print(f" {article['publisher']} | {article['published_date'][:10]}")
print(f" {article['ai_summary'][:200]}...")
print()
```
**Sample response (excerpt):**
```json theme={"system"}
{
"status": "success",
"data": [
{
"ai_summary": "Brex CEO Pedro Franceschi discusses the rapid $5.15 billion deal with Capital One, highlighting the deal's significance in the fintech space, Brex's evolving valuation, and plans for aggressive market competition.",
"type": "Mergers & Acquisitions",
"url": "https://www.youtube.com/watch?v=3a3omxYXqK4",
"title": "How Brex signed a $5.15B Deal in ~40 Days",
"company_name": "Brex",
"publisher": "Youtube",
"published_date": "2026-01-30 00:00:00",
"source": "Wokelo",
"author": "",
"countries": [],
"sentiment": "",
"company_names": [],
"primary_tag": "Mergers & Acquisitions",
"original_language": "",
"secondary_tags": [],
"newsworthiness_impact": "",
"scraped_text": "..."
}
],
"count": 12,
"total": 12,
"limit": 100,
"offset": 0
}
```
### Deal due diligence — target company news sweep
Pull all news for an acquisition target over a defined period, prioritise by category, and exclude press release sources to focus on independent coverage.
```python theme={"system"}
import requests
HEADERS = {"Authorization": "Bearer "}
DD_CATEGORIES = ", ".join([
"MERGERS & ACQUISITIONS",
"LEGAL PROCEEDINGS & LITIGATION",
"REGULATORY APPROVALS & CERTIFICATIONS",
"FINANCIAL DISTRESS & RESTRUCTURING",
"C-SUITE APPOINTMENTS & DEPARTURES",
"EARNINGS RELEASES",
"PARTNERSHIPS & ALLIANCES"
])
# Exclude wire services — focus on editorial coverage
BLACKLIST = ", ".join([
"https://www.prnewswire.com/",
"https://www.businesswire.com/",
"https://www.globenewswire.com/"
])
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=HEADERS,
params={
"company": "tesla",
"category": DD_CATEGORIES,
"start_date": "2024-01-01",
"end_date": "2026-03-31",
"blacklisted": BLACKLIST,
"limit": 200
}
)
data = response.json()
articles = data["data"]
print(f"Retrieved {data['count']} articles (total matching: {data['total']})\n")
# Group by primary tag
from collections import defaultdict
by_category = defaultdict(list)
for a in articles:
tag = a.get("primary_tag") or "Uncategorised"
by_category[tag].append(a)
for category, items in sorted(by_category.items(), key=lambda x: -len(x[1])):
print(f"\n{'='*60}")
print(f"{category} ({len(items)} articles)")
print('='*60)
for a in items[:3]: # Show top 3 per category
print(f" [{a['published_date'][:10]}] {a['title']}")
print(f" {a['ai_summary'][:150]}...")
```
### Competitive monitoring — product launches and partnerships
Track product and partnership news for a set of competitors over a rolling 90-day window.
```python theme={"system"}
import requests
from datetime import datetime, timedelta
HEADERS = {"Authorization": "Bearer "}
COMPETITORS = ["salesforce", "hubspot", "pipedrive", "zoho"]
end_date = datetime.today().strftime("%Y-%m-%d")
start_date = (datetime.today() - timedelta(days=90)).strftime("%Y-%m-%d")
INTEL_CATEGORIES = ", ".join([
"PRODUCT LAUNCHES & ENHANCEMENTS",
"PARTNERSHIPS & ALLIANCES",
"GEOGRAPHIC EXPANSION",
"MERGERS & ACQUISITIONS",
"EQUITY FUND-RAISING"
])
all_articles = []
for company in COMPETITORS:
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=HEADERS,
params={
"company": company,
"category": INTEL_CATEGORIES,
"start_date": start_date,
"end_date": end_date,
"limit": 50
}
)
articles = response.json().get("data", [])
for a in articles:
a["_queried_company"] = company
all_articles.extend(articles)
# Sort all articles by date, most recent first
all_articles.sort(key=lambda x: x["published_date"], reverse=True)
print(f"\nCompetitive Intelligence Feed — {start_date} to {end_date}")
print(f"Total articles: {len(all_articles)}\n")
for a in all_articles[:20]:
sentiment = a.get("sentiment") or "—"
print(f"[{a['_queried_company'].upper()}] [{a['primary_tag']}] [{sentiment}]")
print(f" {a['title']}")
print(f" {a['publisher']} | {a['published_date'][:10]}")
print()
```
### Paginating through large result sets
When `total` exceeds `limit`, paginate using `offset` to retrieve all matching articles.
```python theme={"system"}
import requests
HEADERS = {"Authorization": "Bearer "}
PARAMS = {
"company": "apple",
"start_date": "2024-01-01",
"limit": 100,
"offset": 0
}
all_articles = []
while True:
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=HEADERS,
params=PARAMS
)
data = response.json()
batch = data.get("data", [])
all_articles.extend(batch)
print(f"Fetched {len(all_articles)} / {data['total']} articles")
if len(all_articles) >= data["total"] or not batch:
break
PARAMS["offset"] += PARAMS["limit"]
print(f"\nTotal retrieved: {len(all_articles)}")
```
### Extracting article text for LLM processing
Use `scraped_text` to feed full article content into a summarisation or classification pipeline.
```python theme={"system"}
import requests
HEADERS = {"Authorization": "Bearer "}
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=HEADERS,
params={
"company": "openai",
"category": "MERGERS & ACQUISITIONS, EQUITY FUND-RAISING",
"start_date": "2025-01-01",
"limit": 20
}
)
articles = response.json()["data"]
# Filter to articles with scraped text available
with_text = [a for a in articles if a.get("scraped_text", "").strip()]
print(f"{len(with_text)} of {len(articles)} articles have full text\n")
for a in with_text[:3]:
word_count = len(a["scraped_text"].split())
print(f"Title: {a['title']}")
print(f"Publisher: {a['publisher']} | Words: {word_count}")
print(f"AI Summary: {a['ai_summary']}")
print()
# Pass a["scraped_text"] to your LLM pipeline here
```
### JavaScript / Node.js
```javascript theme={"system"}
const fetch = require("node-fetch");
async function getCompanyNews(company, options = {}) {
const params = new URLSearchParams({
company,
...(options.startDate && { start_date: options.startDate }),
...(options.endDate && { end_date: options.endDate }),
...(options.category && { category: options.category }),
limit: options.limit || 100,
offset: options.offset || 0
});
const response = await fetch(
`https://api.wokelo.ai/api/enterprise/company/news/?${params}`,
{
method: "GET",
headers: {
"Authorization": `Bearer ${process.env.WOKELO_API_KEY}`,
"Content-Type": "application/json"
}
}
);
if (!response.ok) {
throw new Error(`API error: ${response.status} ${response.statusText}`);
}
return response.json();
}
// Example: get M&A news for Tesla in 2025
getCompanyNews("tesla", {
category: "MERGERS & ACQUISITIONS, PARTNERSHIPS & ALLIANCES",
startDate: "2025-01-01",
endDate: "2025-12-31"
}).then(data => {
console.log(`Found ${data.total} articles`);
data.data.forEach(a => {
console.log(`[${a.primary_tag}] ${a.title} — ${a.publisher}`);
});
});
```
***
## 7. Error Handling
The API uses standard HTTP status codes. All error responses include a JSON body with a `detail` or `message` field.
| Status | Meaning | Cause & Resolution |
| --------------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `200 OK` | Success | Articles returned successfully. |
| `400 Bad Request` | Invalid parameters | A required parameter is missing or a value is invalid — e.g. missing `company`, malformed `start_date`, or unrecognised category string. Check the `detail` field. |
| `401 Unauthorized` | Auth failed | The `Authorization` header is missing, malformed, or contains an invalid token. Verify your key in **Settings → API Keys**. |
| `403 Forbidden` | Insufficient access | Your plan does not include access to this endpoint or company. Contact [support@wokelo.ai](mailto:support@wokelo.ai) to review your plan. |
| `404 Not Found` | Company not found | The `company` permalink could not be resolved. Use the [Company Search API](/supporting-apis-doc) to verify the permalink before querying. |
| `429 Too Many Requests` | Rate limit exceeded | Implement exponential back-off. The response includes a `Retry-After` header. |
| `500 Internal Server Error` | Server error | Retry after a brief delay. If the issue persists, contact [support@wokelo.ai](mailto:support@wokelo.ai). |
**Error response example:**
```json theme={"system"}
{
"status": "error",
"detail": "Company permalink 'unknown-co' could not be resolved."
}
```
**Retry logic with exponential back-off:**
```python theme={"system"}
import time, requests
def fetch_news_with_retry(params, api_key, max_retries=3):
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
for attempt in range(max_retries):
try:
response = requests.get(
"https://api.wokelo.ai/api/enterprise/company/news/",
headers=headers,
params=params,
timeout=30
)
if response.status_code == 429:
wait = 2 ** attempt # 1s, 2s, 4s
time.sleep(wait)
continue
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
if attempt == max_retries - 1:
raise
time.sleep(1)
raise Exception(f"Failed after {max_retries} attempts")
```
***
## 8. Best Practices
**Use the `company` permalink, not a display name**
The `company` parameter expects a permalink (e.g. `"tesla"`, `"brex"`) or a full URL. Passing a display name like `"Tesla Inc."` will not resolve correctly. Use the [Company Search API](/supporting-apis-doc) to look up the permalink for any company:
```python theme={"system"}
# Resolve permalink first
search = requests.get(
"https://api.wokelo.ai/api/enterprise/company/search/",
headers=HEADERS,
params={"query": "Tesla Inc"}
)
permalink = search.json()["results"][0]["permalink"] # "tesla"
```
**Filter by `category` to reduce noise — this is the highest-impact parameter**
Returning all news for a major company like Tesla or Apple can yield hundreds of articles, many of which are market data reports, stock price articles, or tangentially related coverage. Use the `category` filter aggressively to surface only the signal types relevant to your workflow. Combine multiple categories with a comma:
```python theme={"system"}
# For portfolio monitoring — adverse signals only
params["category"] = (
"LEGAL PROCEEDINGS & LITIGATION, "
"WORKFORCE REDUCTIONS, "
"FINANCIAL DISTRESS & RESTRUCTURING, "
"CYBERSECURITY INCIDENTS, "
"C-SUITE APPOINTMENTS & DEPARTURES"
)
```
**Exclude press release domains with `blacklisted`**
Wire service articles (`prnewswire.com`, `businesswire.com`, `globenewswire.com`) are often company-issued press releases that add volume without independent editorial perspective. Blacklist them for competitive intelligence and monitoring use cases where you want journalist-authored coverage:
```python theme={"system"}
params["blacklisted"] = (
"https://www.prnewswire.com/, "
"https://www.businesswire.com/, "
"https://www.globenewswire.com/"
)
```
**Use `newsworthiness_impact` to triage before reading**
When processing large result sets, filter to `"High"` impact articles first for executive briefings or urgent monitoring workflows. `"Medium"` and `"Low"` articles can be batch-processed later:
```python theme={"system"}
high_signal = [a for a in articles if a.get("newsworthiness_impact") == "High"]
```
**Guard against empty classification fields**
`primary_tag`, `sentiment`, `newsworthiness_impact`, and `original_language` can be empty strings for older articles or articles still pending classification. Always use `.get()` with a default rather than direct key access:
```python theme={"system"}
# ❌ KeyError risk when field is present but empty
if article["sentiment"] == "Negative":
...
# ✅ Safe
if article.get("sentiment") == "Negative":
...
```
**Use `scraped_text` for downstream LLM pipelines — but expect some gaps**
`scraped_text` contains the full article body and is ideal for passing to summarisation, classification, or embedding models. However, paywalled articles (Bloomberg, FT, WSJ) will often have empty or truncated `scraped_text`. Use `ai_summary` as a fallback:
```python theme={"system"}
content = article.get("scraped_text", "").strip()
if not content:
content = article.get("ai_summary", "")
```
**Paginate correctly when `total` exceeds `limit`**
The default `limit` is 100 and the maximum is 1000. For queries returning large result sets, check `total` against `count` and paginate using `offset` until all records are retrieved. Stop when `count` returns 0 or `len(all_articles) >= total`.
**Use `company_names` to map article networks**
The `company_names` array resolves all entity mentions in the article, not just the queried company. This lets you identify deal counterparties, named investors, or competitors without text parsing — useful for building relationship graphs across a coverage universe.
***
## 9. Related APIs
Monitor the latest news across an entire industry or sector, rather than a single company.
Retrieve a curated, cross-company newsfeed based on a topic, theme, or portfolio watchlist.
Generate a comprehensive AI intelligence report on any company — strategy, financials, and competitive position.
Synchronously enrich firmographic and financial data for any company by permalink or URL.
Identify and score potential acquisition targets — AI-ranked with deal feasibility, synergy, and precedent scores.
Company Search, Request Status, and other utilities used alongside monitoring workflows.