Case study • Media & Entertainment • Visual Content Moderation
Enhancing the Processes
and Knowledge Sharing for a Leading Visual Media Bank
We partnered with a global stock photography platform managing over 700M+ assets to overhaul its content moderation, QA, and knowledge-sharing processes. By streamlining operations, deploying training modules, and building automated alert systems, we helped reduce moderation errors to just 0.05%, enabling rapid scaling and business expansion.
Success Highlights
Key Details
Industry: Stock Photography / Visual Media
Geography: US
Platform: Content moderation system
Business Challenge
Managing a content ecosystem of over 700 million media files required more than manpower — it required structured processes, scalable workflows, and intelligent knowledge-sharing to keep pace with global growth.
Our Solution Approach
We created a quality-first, scale-ready moderation system — built with automation, governance, and continuous learning at its core.
1 · Discover
Audit Workflow & Feedback Loops
Analyzed content flow from upload to publication, identifying process blind spots, manual handoffs, and inconsistent enforcement of moderation guidelines.
2 · Consolidate
Define Roles, Training & Review Protocols
Created role-specific QA playbooks and moderation protocols. Standardized annotation layers across image and video, and trained teams via modular, repeatable sessions to ensure consistent tagging and review.
3 · Automate
Integrate Alerts & Auto-Audit Pipelines
Built alert scripts to flag anomalies in user-submitted content (e.g., suspicious tags, NSFW patterns) and set up auto-validation jobs that ran nightly to catch schema violations, mislabels, or missing metadata.
4 · Accelerate
Enable High-Volume, High-Quality Curation
Added capacity to launch 21+ content queues with continuous QA scoring. Review dashboards gave real-time insight into backlog, rejection reasons, and annotator performance—supporting confident expansion.
Technical Highlights
Automated moderation alerting system for suspicious content and behavioral anomalies using real-time rule-based triggers
Text classification and image tagging models integrated for content filtering and priority escalation
Custom Python scripts for bulk content audits, file validations, and annotation consistency checks
Version-controlled repository for storing moderation configurations, annotation schemas, and QA benchmarks
Scheduled compliance audit automation leveraging cron jobs to enforce regulatory checks and generate audit logs
Data processing pipeline for ingesting, validating, and cataloging large volumes of visual content across formats (images, video, illustrations)
def process_submission(content):
if not content.meets_quality_standards():
log_issue(content.id, “Quality check failed”)
send_alert(“QA_Team”, content)
return “Rejected”
if content.contains_prohibited_tags() or is_flagged_by_model(content):
quarantine(content)
notify_compliance_team(content)
return “Flagged for Review” approve_for_publication(content)
return “Approved”
Business Outcomes
0.05%
Error Rate
Automation and QA oversight reduced moderation mistakes to near-zero.
2X
Faster Market Presence
With support processes offloaded, the client scaled content and territory reach.
21
New Content Queues Launched
Successfully curated new categories like video footage, expanding platform depth.
Want to Scale Quality Moderation Without Sacrificing Accuracy?
Let’s talk about building moderated workflows that improve over time — with measurable trust, QA ownership, and expansion-ready infrastructure.