Rather than randomly selecting neural networks to classify a media file, the conductor can determine which neural network engines (from the conductor ecosystem of neural networks) are the best candidates to classify a particular portion/segment of the media file (e.g., audio file, image file, video files). The best candidate neural network engine(s) can depend on the nature of the input media and the characteristics of the neural network engines. In object recognition and identification, certain neural networks can classify vehicles better than others, while another group of neural networks can classify structures better. The conductor can take out the guess work and construct in real-time an inter-classifier neural network using one or more layers selected from one or more pre-trained neural network, based on attribute(s) of the media file, to classify the media file.