14 agents filtered 120,000 influencers down to 30 and improved CTR by 2.7% — multi-agent in production

Description and use

A 14‑agent AI pipeline automated the entire influencer‑marketing workflow—data collection, selection, strategy, content, media buying, and settlement—boosting CTR by 2.7 % and workflow efficiency by 30 %.

Problem

Influencer marketing workflows are manual, fragmented, and coordination-heavy, leading to slow, inconsistent, and costly campaigns.

Outcome

The multi-agent system reduced influencer selection from 120k to 30 with consistent scoring, personalized briefs, automated media buying, and end-to-end settlement, yielding a 2.7% CTR increase and 30% workflow efficiency improvement.

Workflow

• Connect data collection agents to platform APIs for audience and performance data
• Scan ~120,000 influencer profiles and score them on style, audience overlap, conversion, and pricing
• Select top 30 influencers based on multi-dimensional ranking
• Generate 60-point, per-influencer strategy briefs tailored to content format and audience
• Produce text, image, and video assets aligned with briefs via coordinated agents
• Auto-purchase and optimize ad placements through platform APIs, adjusting spend in real-time
• Track performance from impression to lead to payment and settle payments automatically

Evidence

120,000 to 30. That's a 99.975% reduction. A human team doing this manually might review a few hundred profiles in a day if they're fast.

Source

After a 1-month trial run: Natural click-through rate improved by 2.7% compared to comparable campaigns

Source