Under the hood, a fine-tuned Vision Transformer identifies 227 species from images and video frames, reaching 75.9% validation accuracy. An Audio Spectrogram Transformer identifies 72 species from sound, reaching 68.3%. An LLM only explains the result and never makes the identification, so a language model can't override a trained classifier. Uploads go to storage and inference in parallel to keep latency low, and low-confidence results are flagged for human verification.