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  • IDB Platform
  • stuv ai


IDB Platform

Intelligent big data platform

Provides excellent performance and analysis quality for fusion analysis of structured and unstructured big data

The optimal platform for real-time analysis and prediction of stream big data, as well as semantic search/analysis and intelligence for big data, and for implementing data-based smart data and next-generation IT systems, including intelligence and operational intelligence building.

A platform that integrates core infrastructure technologies such as natural language processing (NLP), artificial intelligence technology, machine learning, deep learning, reasoning, and distributed parallel processing, providing users with step-by-step engines and various analysis and visualization functions throughout the life cycle of big data.


System Engine

Semantic Search
Text Mining
Stream Analysis
Cognitive Analysis
Visual Analysis


mLab Intelligent big data platform
Visual intelligence
  1. Image recognition
  2. Situation/Behavior Understanding
  3. Incident/disaster detection
Language intelligence
  1. AI consultation, chatbot
  2. In-depth Q&A
  3. Voice recognition, translation
Emotional intelligence
  1. Emotion/Reputation Analysis
  2. Social issue detection
  3. Personalized recommendations
Learning reasoning intelligence
  1. Language/knowledge learning
  2. Prediction/Early detection
  3. Complex logical reasoning



STUV Artificial Intelligence

STUV AI established Data Technology through professional research based on state-of-the-art deep learning technology.
The technology of developing artificial intelligence through deep learning techniques and data handling such as video and video properties and image, voice, Tag, and text is the source of SUTV AI technology.

  • BoundingFinding the desired object and creating a bounding box.(Quad/Polygon method)
  • LandmarkFind the object you want to recognize in the image and express it as a point on the outline.
  • ClassificationClassifying the properties of an image.
  • CollectionCollecting various video content properties.



Recommended Curation STUV

Based on STUV AI, it provides various types of recommended curation such as content curation, data curation, curation shopping, curation commerce.

SEEKFind, collect, and expand information for the latest in data.
SENSEOrganize, archive, and visualize information by filtering information.
SHAREShare information with networks and SNS.


  • Provide optimal customized video content of users through AVT (Audio/Video/Text) information filtering technique using AI technology from various video resources.
  • Provide recommended curation through analysis of user patterns of data mining techniques such as clustering, classification, and association rules.
  • Provide customized video and customized product information about similarity analysis of a language analysis engine based on machine learning/deep learning that processes text analysis functions such as morphological analysis, object name recognition, parsing, and emotional analysis for processing unstructured data.


Video Commerce

Video Commerce

It’s a technology that induces interesting purchases on video with data built on the basis of STUV AI’s Data Technology, provides with clients whose purpose are to increase sales through RCNN-based video product matching and shopping through the construction of video data that combines MCNV (Multi Channel Network Video) according to the client’s format.


mLab Technology features
  • Detect products, brands and similar products in video
  • Identify people in the video using Haar algorithm and provide search results
OCR, Analyze Image & Video
  • Quality improvement and correction with self-developed preprocessing technology
  • Process multiple images simultaneously with parallel processing algorithm
  • Using self-developed deep learning video learning algorithm
Product matching in the distribution industry
  • Analyze and list words through priority according to frequency of data extracted from video
  • Matching products in representative product categories with video-based data
  • Matched products through word analysis
  • Related products with brand exposure during word analysis
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