A system comprising: one or more processors; and one or more non-transitory computer-readable media configured to maintain executable instructions, which, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving from a device (104, 108), speech sound patterns (136, 175) corresponding to a voice input (134, 174) related to equipment; determining an identity of a person associated with the device (104, 108); identifying the equipment related to the voice input (134, 174); using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input (134, 174) is classified as normal or non-normal; providing at least one of the received speech sound patterns (136, 175) or a text conversion of the speech sound patterns (136, 175), along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input (134, 174); composing a text instruction for responding to the voice input (134,174) based on the determined response to the voice input (134, 174); converting the text instruction to an audio speech file; and sending the audio speech file to the device (104, 108) for presentation on the device (104, 108). one or more processors; and one or more non-transitory computer-readable media configured to maintain executable instructions, which, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving from a device (104, 108), speech sound patterns (136, 175) corresponding to a voice input (134, 174) related to equipment; determining an identity of a person associated with the device (104, 108); identifying the equipment related to the voice input (134, 174); using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input (134, 174) is classified as normal or non-normal; providing at least one of the received speech sound patterns (136, 175) or a text conversion of the speech sound patterns (136, 175), along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input (134, 174); composing a text instruction for responding to the voice input (134,174) based on the determined response to the voice input (134, 174); converting the text instruction to an audio speech file; and sending the audio speech file to the device (104, 108) for presentation on the device (104, 108). receiving from a device (104, 108), speech sound patterns (136, 175) corresponding to a voice input (134, 174) related to equipment; determining an identity of a person associated with the device (104, 108); identifying the equipment related to the voice input (134, 174); using an emotion-recognition machine learning model customized for the identified person to determine whether an emotion classification associated with the voice input (134, 174) is classified as normal or non-normal; providing at least one of the received speech sound patterns (136, 175) or a text conversion of the speech sound patterns (136, 175), along with an equipment history of the identified equipment and an indication of the emotion classification as input to one or more additional machine learning models to determine a response to the voice input (134, 174); composing a text instruction for responding to the voice input (134,174) based on the determined response to the voice input (134, 174); converting the text instruction to an audio speech file; and sending the audio speech file to the device (104, 108) for presentation on the device (104, 108).