Track Any Entity Across the Intelligence Cycle

Every news story involves entities — people, companies, countries, technologies. But knowing that “NVIDIA” appeared in a headline today isn't useful. Knowing that NVIDIA mentions jumped 300% this week, peaked on Tuesday, and are now falling — that's intelligence.

Polaris now tracks entity mention timelines across a 14-day window with automatic trend detection.

What You Get

For any entity, Polaris shows:

Example: Tesla

That tells you the Tesla story cycle from last week is cooling down. An agent monitoring Tesla would know to deprioritize it and shift attention to rising entities.

Use It in Your Agent

from veroq import PolarisClient

client = PolarisClient(api_key="demo")

# Get entity timeline + briefs
result = client.entity_briefs("NVIDIA")
print(f"Trend: {result.trend}")
print(f"Peak: {result.peak} mentions")

for day in result.timeline:
    print(f"  {day['date']}: {day['mentions']} mentions")

# Alert on rising entities
trending = client.trending_entities(limit=10)
for entity in trending:
    data = client.entity_briefs(entity.name)
    if data.trend == "rising":
        print(f"ALERT: {entity.name} is rising")

Combine with Trending Entities

The real power is combining timelines with the trending endpoint. Your agent can:

1. Check trending entities every hour
2. Fetch timelines for each
3. Alert only on entities that are both trending AND rising
4. Ignore entities that peaked days ago and are now falling

That's the difference between “here's what's in the news” and “here's what's about to matter.”

See It Live

Browse trending entities with timelines or check any specific entity. Every data point is available via API. Free tier: 1,000 calls/month.