Harvester has a multi-service architecture that allows users to quickly, flexibly and efficiently build automated systems for deep processing of audio, text and visual data.
Harvester works with large volumes of data, which will be uploaded to the Harvester storage itself or to some external storage to which the Harvester will have access. The Harvester can also store the results of its work in an external storage that the user will define. So, if necessary, Harvester will not have access to the internal processes of already existing systems of the customer. That is, the Harvester can be quite easily and painlessly integrated into the already existing infrastructure of information collection and processing. At the same time, the operation and security of this system will not be disturbed.
Files selected and structured according to specified criteria with which analysts and investigators will work in the future. That is, from the entire vast array of data, only the necessary files will be selected, in which the data required for analysis will be highlighted.
An indexed database n which tags will be assigned to each file depending on what information is there. In the future, the analyst can make requests to this database himself and receive the files he needs at the moment. This speeds up the work of analysts and gives them flexibility in working with data.
Harvester accelerates processing and analysis of large volumes of data for faster decision-making and better insights in intelligence.
Harvester has integrated turnkey solutions that are based on multiple advanced and proven technologies. These solutions have been developed in cooperation with world-leading research laboratories that specialize in Automatic Speech Recognition (ASR) and Natural Language Processing / Understanding (NLP/NLU). We using state-of-the-art technologies developed by leading experts, which help to enhance the accuracy and reliability of data processing.
Key Technologies
Interface examples (TBD)
Face Recognition (FR)
Objects Detection & Identification (ODI)
Speaker Recognition (SR)
Gender Identification (GI)
Language Identification (LI)
Speech-to-Text Transcription (S2T)
Language Identification (LID)
Keyword Spotting (KWS)
Machine Translation (MT)