Automated Content Processing Pipeline

Project Overview

Pocstock, a diversity-focused stock photography platform, needed to replace its slow, manual review process. I designed an AI-powered automation pipeline in n8n to cut processing times, improve metadata quality, and scale contributor uploads.

The system is in testing, with early results showing faster approvals and significant cost savings.

Problem

Pocstock, a growing stock photography platform specializing in diverse and authentic content, faced major bottlenecks in its content review process. Each contributor submission took an average of 45 minutes to process, leading to approval delays of three to five days.

This manual workflow not only slowed down the availability of new content but also resulted in inconsistent metadata, uneven diversity validation, and contributor frustration. As upload volumes increased, the inefficiencies made it clear that scaling with the existing system would not be sustainable.

Solutions

To address these challenges, I designed an AI-powered automation pipeline using n8n. The system consolidates multiple submission channels into a single intake, then applies automated quality checks and diversity compliance validation through AWS Rekognition and a custom scoring algorithm. Metadata is generated with GPT-4o to ensure SEO-friendly, consistent descriptions, while a smart routing system automatically approves compliant submissions and forwards flagged content for human review.

Contributors receive instant notifications and feedback, while business intelligence dashboards provide real-time insights into workflow efficiency. This solution not only reduces processing times dramatically but also establishes a scalable foundation for Pocstock’s continued growth.

Impact

In early testing with small sample submissions, processing time was reduced from 40 minutes to under 5, with 80% fewer manual reviews and same-day content availability. AI-generated metadata improved search alignment by up to 40%, and contributors in the pilot test reported faster feedback and clearer approvals.

While these results are promising, the AI analysis pipeline is still undergoing training to refine cultural sensitivity detection and ensure higher accuracy. Once validated with real contributor data, the system is projected to handle 10x more submissions at lower operational costs, positioning Pocstock for scalable growth.