How We Built an AI Clip Pipeline at $0.055 Per Clip

The Problem

Gaming content creators spend 4-6 hours manually editing clips from raw gameplay footage. We needed an automated system that could detect epic moments, cut HD/4K clips, add captions, and publish — all without human intervention.

Our Approach: 4-Signal AI Fusion

Single-signal detection (just audio peaks, or just visual changes) missed 40% of actual highlights. We designed a fusion system that combines:

**Vision Analysis** — Scene change detection, kill feeds, HUD events

**Audio Peaks** — Gunshots, explosions, crowd reactions

**Facecam Emotion** — Streamer reactions correlated with gameplay

**Cluster Detection** — When 3+ signals align within a 5-second window

The Stack

**FastAPI + Celery** for async processing pipeline

**Modal.com** for serverless GPU inference

**PostgreSQL** for job tracking and metadata

**Cloudflare R2** for video storage

Results

**$0.055 per clip** (HD) / $0.077 (4K)

**10x cheaper** than self-hosted GPU servers

**64 production sprints** delivered

**101 game profiles** with game-specific AI tuning

Key Takeaway

The biggest lesson: don’t over-engineer the AI. Start with simple heuristics, measure accuracy, then add complexity only where it improves results. Our first prototype used only audio peaks and caught 60% of highlights. Adding vision got us to 85%. The final fusion approach reaches 95%+.