Curious how companies are using Sage? We're publishing a new case study every day.
Today we’re featuring
@capacitr_xyz, a mobile app that turns news and social posts into trading signals, with a chat agent for execution.
Capacitr uses Sage across six decision gates:
1. Content filtering
2. Web research
3. Market ranking
4. Trade guardrails
5. Chat scope
6. Signal freshness.
Sage evaluates whether incoming content merits processing and whether it needs fresh research.
That prevents low-value items from consuming expensive model and search calls.
At full enforcement, Capacitr projects:
• 44% lower LLM token spend, avoiding 1.6B+ tokens per month.
• 50% lower web research costs, avoiding approximately 19,500 metered search calls per month.
• $500+ in monthly compute savings.
The case study covers the integration, how the team sets enforcement thresholds, and how caching keeps freshness checks outside the feed’s read path.
Links below.