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For what purposes and areas of application was the AI system developed?

Aiconix's AI system is designed for media production and video editing, automating processes like content analysis, transcription, video editing, and knowledge extraction using technologies such as deep learning, computer vision, and NLP. It is modular, cloud-ready, and integrates easily into existing workflows.

Aiconix AI System Overview

Aiconix developed its AI system specifically for use in media production and video editing. It automates and enhances the media content production process using artificial intelligence and integrates AI processes into existing workflows and software via an interface. The system utilizes advanced AI techniques such as deep learning, computer vision, and natural language processing (NLP), and can be provided as a cloud-based or cloud-agnostic service.

Key Objectives and Application Areas:

1. Content Analysis and Optimization:
Aiconix’s AI analyzes content to identify the most important or relevant parts. Based on this metadata, highlights or summaries can be generated, or tagging for archiving can be automated.
AI methods used:

  • Sentiment analysis with NLP for text evaluation

  • Visual pattern recognition with deep learning

  • Keyframe extraction with computer vision

  • Speech-to-text (STT) with deep learning

  • Machine translation with NLP

  • Automatic speech recognition (ASR)

2. Video Transcription and Subtitling:
Aiconix provides solutions for real-time transcription of spoken text and the creation of subtitles. This is especially useful for making live content more accessible and multilingual.
AI methods used:

  • Speech-to-text (STT) with deep learning

  • Machine translation with NLP

  • Automatic speech recognition (ASR)

3. Automated Video Editing:
Aiconix offers AI-based tools to support video editing, such as automated video cutting. This reduces manual effort and saves time and resources.
AI methods used:

  • Computer vision for image analysis

  • Visual pattern recognition with deep learning

  • Speech-to-text with deep learning

  • Machine translation with NLP

  • Automatic speech recognition

4. Knowledge Provision:
In addition to the above services, knowledge can be provided via an integrated knowledge graph. This can be linked with previous analysis results in a vector database to identify search queries and correlations more efficiently.
AI methods used:

  • Knowledge Graph