English
Automated content moderation on standard marketplaces routinely fails by missing critical threats—such as Human Trafficking patterns that standard filters overlook within textual noise. To close this blind spot, our architecture deploys a dedicated global engine specifically trained to detect exploitation and human trafficking signatures that other platforms miss. Instead of trusting moderation to a single "blind" neural network, we decouple probabilistic model inference from operational decision-making, shifting the paradigm from probabilistic guessing to deterministic system governance.
The platform architecture spans 7 regional language contours: Western European, Central & Eastern European, Baltic, CIS, Nordic, Balkan, and Caucasus. Each contour deploys a dedicated 3-tiered engine stack: a base semantic classifier, a character/slang obfuscation detector, and a BERT-based contextual filter for identifying hidden intent. All 22 models operate as independent signal generators, assembling a comprehensive scoring vector without directly triggering content blocks.
Final classification authority belongs strictly to a deterministic Policy Layer, which correlates scoring vectors against platform rules to categorize content into Approved, Pending Review, or Blocked states. This eliminates single points of failure and minimizes false positives to near-zero. Sensitivity thresholds are calibrated independently per engine at runtime, adapting dynamically to textual noise, character obfuscation, and structural input anomalies to guarantee high availability and operational safety.
