Real-time content moderation that understands context, not just keywords.
A platform handling 50,000+ pieces of user-generated content daily relies on keyword blocklists and basic image classifiers. Keyword filters generate massive false positive volumes — "kill" triggers in gaming, cooking, and sports contexts. Meanwhile, sophisticated violations using coded language and context-dependent harassment bypass the rules.
Hive Moderation demonstrated automated moderation performing at or above human accuracy across text, images, and video. Their improved models now outperform human moderators on consistency. Spectrum Labs focuses on contextual toxic behavior understanding. The Digital Services Act (EU) requires "expeditious" content review.
Human moderation is not scalable: 30-50% annual turnover from burnout and genuine psychological harm from exposure to harmful content.
This agent processes content in real time across text, images, and video. For text, fine-tuned language models evaluate content in context — "kill it" means different things in a gaming community versus a direct message. For images, computer vision detects nudity, violence, and policy-violating content. For video, keyframe and audio transcript analysis.
Each item receives per-policy confidence scores across configured categories: hate speech, harassment, violence, sexual content, spam, misinformation, and custom categories. High-confidence violations are auto-actioned. Borderline cases queue for human review with AI assessment and reasoning.
The system adapts to your community norms. A medical education platform has different policies than a children's app. Custom categories added without retraining base models.
We deploy a multi-modal pipeline on FastAPI backed by Kafka for ingestion — text goes through a fine-tuned language model for semantic policy classification, images through a PyTorch vision model, and video through keyframe extraction plus audio transcription before both branches merge into a unified decision layer. Policy categories are mapped from your community guidelines and stored in PostgreSQL with per-category confidence thresholds; borderline decisions are queued for human review, and moderator outcomes feed back into retraining. Custom categories can be trained on your historical moderation data in the same setup window. Setup takes 2–3 weeks from guideline handoff to production traffic.
Text, images, and video are processed through separate model branches — LLM-based semantic analysis, computer vision for imagery, and keyframe plus audio analysis for video — before results are merged into a single policy decision per content item. Each modality runs in parallel via Kafka consumers so throughput scales independently.
The same phrase can be benign in one forum and a clear violation in another. The agent factors in platform context, thread history, and user standing when scoring content — not just the isolated text or image. This reduces false positives on edge cases that keyword filters and out-of-the-box classifiers consistently get wrong.
Standard policy categories (hate speech, NSFW, spam, self-harm) ship pre-configured, and custom categories can be trained on your moderation history. Each category carries an independent confidence threshold — auto-remove at 0.95, queue for review at 0.70 — so high-stakes violations never wait in a queue.
Borderline cases route to a structured review queue with the AI's confidence breakdown and the specific policy category flagged, so moderators make faster decisions with full context. Moderator verdicts are written back to PostgreSQL and used in periodic retraining cycles, so model accuracy improves on your platform's actual content distribution.
Anonymized work with hard metrics — NDA-bound, no client names.
87%
Auto-Classification Rate
3.2 days
Avg Turnaround (from 15)
2.1%
Misroute Rate (from 18%)
“The misrouting rate was the metric that mattered internally — every misrouted document created rework cycles that consumed staff time and delayed the original applicant. Getting that from 18% to 2% changed the entire operations picture.”
— Director of Digital Services, Regional Government Department
94%
Classification Accuracy
340ms
Avg Processing Time
78%
Manual Review Reduction
“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 Platform
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call
Fordel Studios