AI Video Ad Performance Analyzer

Creative teams have always had to guess at why one ad outperforms another. This tool makes that legible. Video Analyzer is an invite-only platform that takes a CSV export from Google Ads or Meta, pulls in the corresponding video or image assets, and runs a battery of computer vision and audio analysis — then surfaces which creative elements actually correlate with performance.

How it works

The architecture is a three-layer stack: a Next.js frontend deployed on Netlify, a Python backend running on Modal.com for serverless GPU-accelerated processing, and Supabase for auth, database, and team management. When a user uploads a CSV, the backend spins up containers that download each asset via yt-dlp, then runs OpenCV, MediaPipe, and EasyOCR across frames — extracting dominant colors, brightness, face counts, on-screen text, motion energy, and audio sentiment via librosa and TextBlob. Results are stored and joined back to the performance data in Supabase.

What you actually see

The dashboard surfaces the analysis in layers: top-level creative performance metrics, a breakdown by campaign, asset-level analysis with the ability to click through and watch each video inline, and an AI-generated observations and recommendations panel. Teams can be organized by brand workspace, with role-based access and invite flows — so an agency can manage multiple clients in one place.

Why it matters

This kind of tooling has historically lived inside expensive enterprise platforms or required a data science team to build and maintain. By connecting off-the-shelf computer vision libraries to a serverless Python backend and a clean authenticated frontend, this becomes something a lean creative team can actually run themselves. The same pattern applies far beyond ads: content performance analysis for organic video, brand consistency audits across campaigns, or competitive creative intelligence — anywhere you need to go from raw video to structured creative insight at scale.