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Capacitr uses Sage to save money on market analysis

Six structured decisions help Capacitr filter market noise, focus research, and check its trading agent before expensive work begins.

The white Capacitr symbol floats through six marble archways rendered in ultramarine computational textures.

Projected monthly impact

~44%Lower pipeline LLM spend

1.6B+ downstream tokens not used per month

50%Lower web research costs

19,500 metered search calls avoided per month

$495Estimated net savings

Per month, after Sage usage

Capacitr maps news and social posts to trading opportunities, then lets users research and act through a chat agent. Sage decides whether each item needs analysis or whether existing research is enough.

The white Capacitr symbol floats through six marble archways rendered in ultramarine computational textures.

Levanto Sage acts as the intelligent front door for our entire AI pipeline. By making instant, micro-cent decisions on what’s actually worth processing, it cuts out untradeable noise and cuts our downstream AI and search API costs in half, all while keeping response times practically instant.

Utkarsh Bhimte
Co-Founder & CTO, Capacitr

Filtering before the expensive work happens

In a 30-day sample, 11,437 of 25,743 classified items produced no tradeable markets. Each had already incurred extraction, enrichment, matching, and judging costs.

Capacitr’s savings figures account for Sage’s publish gate filtering approximately 380 such items per day before downstream analysis. A separate enrichment gate evaluates whether fresh web research is needed, avoiding an estimated 19,500 metered search calls per month.

Inside Capacitr

Capacitr’s narrative feed, trading agent, and signal detail on three mobile screens.
Screenshot of Capacitr’s public product website. Discover and trade the news like a professional.

Six decisions in application code

SurfaceQuestionWhat the app does
Publish gateTagsClassify content before market analysis.
Enrichment gateYes/NoDecide whether fresh research is needed.
Market rankingSortOrder matched markets by confidence and remaining profit potential.
Trade guardrailChoiceCheck that calls to eight money-moving tools trace to the user’s request.
Scope gateChoiceCheck whether a chat turn is in product scope.
Signal freshnessScale, 0–4Assess whether a catalyst has passed or been overtaken.

Each gate ran in shadow mode first. Feed quality on production data held, so the team switched the gates on.

Observed in the integration

  • Under 100 ms per Sage decision, as reported by Capacitr across its six integration surfaces.
  • Zero observed false positives across 152 audited money-moving tool calls.
  • Zero reported vendor-side incidents over two months of production traffic.

Source

Capacitr’s integration write-up and all figures, supplied by its team, September 2026.

Explore Capacitr ↗

  • Decision gates
  • Content relevance
  • Guardrails

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