Fordel StudiosAI Content Moderation for User-Generated Platforms
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
50,000+ posts per day requiring moderation review. Manual moderation team averaging a 4-hour review latency. Policy violations slipping through due to volume overwhelm, and moderator burnout from sustained exposure to high-volume harmful content.
Content moderation accuracy is uniquely consequential in both directions: false positives suppress legitimate speech, while false negatives allow harmful content to persist. The threshold configuration needed to be different by category — the cost asymmetry between false positives and false negatives is different for graphic violence than for spam. The multi-modal nature of the problem added complexity: text-only classifiers miss image-based violations, and image-only classifiers miss harmful text that uses benign imagery. The system needed to evaluate both modalities and combine their signals correctly. Processing latency was also a hard constraint — posts needed to be classified before public display, which meant the pipeline had to complete in under 500ms end-to-end even during peak posting periods (which create traffic spikes 3–4x above average).
How we
built it
- 01
Audited three months of false positives and false negatives from the existing keyword moderation system to characterise the decision complexity — what percentage of violations required contextual understanding versus simple pattern matching.
- 02
Built a multi-class classification system with confidence scoring, designed to auto-action high-confidence violations and route borderline cases to human review rather than attempting to eliminate the human review queue entirely.
- 03
Implemented context-window analysis that evaluated content in the context of the surrounding thread, user history, and content category — reducing false positives on legitimate content that contained flagged terms in acceptable contexts.
- 04
Deployed with a mandatory human review layer for appeals, ensuring that automated decisions could be reversed with a clear audit trail for both platform accountability and user transparency.
This engagement built a content moderation pipeline that processes posts containing text, images, or both against a configurable policy ruleset. The classifier handles seven violation categories: graphic violence, sexual content, hate speech, harassment, spam, misinformation, and self-harm content. Routing logic applies confidence thresholds per category — auto-approve when all categories score below their safe threshold, auto-reject when any category scores above its reject threshold, and route to human review when any category scores in the ambiguous range. The threshold configuration is maintained in the operations dashboard, allowing policy teams to adjust sensitivity per category without code changes. At production volume, the pipeline processes 50,000+ posts per day with average processing time of 340ms per post.
What we
delivered
Multi-modal content classifier handling text and image inputs, with confidence-based routing that auto-approves clearly safe content, auto-rejects clear violations, and routes edge cases to human review. Human review effort concentrated on ambiguous cases only.
Measurable
outcomes
- Classification accuracy reached 94% on the held-out validation set across all moderation categories.
- Average processing latency reached 340ms per post, enabling near-real-time moderation of the full post volume without batch processing delays.
- Manual review queue reduced 78% as high-confidence cases were auto-actioned, allowing the moderation team to focus on the genuinely ambiguous cases where human judgement adds value.
“The context understanding is the part that changed the team's view of AI moderation. It is not just pattern matching — it is understanding that the same phrase can be a policy violation in one context and completely acceptable in another.”
— Trust and Safety Lead, User Content PlatformReady to build
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