We describe AI-Monitors, our system for the ChildSafeAds shared task at NLLP@EMNLP 2026. The task labels sponsored segments in YouTube videos aimed at teenagers with their commercial type, product category and compliance-risk flags, and provides the input at four levels of increasing collection cost, from the free transcript to a crawled product page. Our system gives each sub-task its own input level and model: a fine-tuned ModernBERT-large encoder reading all four levels predicts commercial type and product category, the latter averaged with a TF-IDF classifier; a second encoder reading only the transcript predicts the compliance flags, averaged on the two disclosure flags with a QLoRA-tuned Qwen2.5-7B-Instruct that also reads the video metadata, before rules from the task’s taxonomy are applied. The system scored 0.6223 mean macro-F1 on the test set, 11th of 21 teams, and labels the 503 test instances in 25.5 GPU-minutes. We report the experiments behind each design choice: in our runs the crawled page helped product categorisation but not compliance flagging, the taxonomy’s definitions helped a prompted model while the legal provisions added little, and in two cases our validation procedure was misleading.