A.I. for Human Life.
our service.

Differentiated Service of MoneyBrain A.I. Chatbot Solution.

Communication Service
in Natural Language

Process questions and tasks in natual language other than command language. Now, experience the services from AI secretary without having to find a complex menu.

Continuous conversation learning using
artificial intelligence technology

There is a limit to the simple pattern matching based chatbot service. Through Artificial Intelligence learning, we have implemented technology that improves the quality of conversation as the service continues.

Task Completion through Interworking with
Existing System

Chatbot can cover complex tasks if interworks with existing systems. Build your own smart business chatbot which can be applied in various way.

Available on a variety of Channels

It can be applied to various messaging platforms such as KakaoTalk, Line, Facebook, etc. with one chatbot scenario developmeent, and it provides API that can be applied in its own app.
Deep Learning

The World's Best Conversational A.I.

RNN based Conversational AI

Our conversation AI is based on Long Short-Term Memory (LSTM) neural network and Gated Recurrent Units (GRU), kinds of Recurrent Neural Networks (RNNs). It learns and seeks the optimal response for a given input. The basic engine chooses the best algorithm and combines it with the existing rule-based learning engine to continuously improve the performance. In addition, the multi-language module and the domain-specific module added on the basic engine helps to retrieve the best answer of the converstional AI.

Dialog Generative Model: Next generation dialog engine

Beyond looking for an optimal answer among existing responses, our next generation conversation AI goes creating a response, similar to a human. Our next-generation AI engine is based on the seq2seq model and replies by creating answer from scratch. Currently, our next-generation engine is used for creating multi-language dialogs to train the retrieval model of the conversational AI agents, and will gradually upgrade the performance to replace the actual conversation engine.

Deep Learning Based Sentiment/Emotion Analysis

We use text classification technique based on the Convolutional Neural Networks (CNNs) to analyze the speakers' status. We reports the polarity of speakers sentiment and as well as the various emotional features such as happiness, sadness, fear, and so on. In addition, our conversational analytics model also helps us to understand the intent of the user's utterance and to enable the AI ​​agent to perform smooth conversations in specific domains such as finance, shopping, and medical care.

Optimum Conversation Analysis

We transcend artificial intelligence that simply communicates. The user's emotions, intentsions, contextual infromation from previous conversations is analyzed to give guidance of the optimum answer. Furthermore, conversations collected from telephone / voice counseling, call center suggest an optimal response strategy through real-time deep learning based dialog analysis.

Speech Recognition / Synthesis

We have transferred the voice recognition technology (STT) from ETRI, and improve the recognition accuracy by using conversation corpus in specific domains. Beyond the text-based chatbot type of the conversational AI, we plan to provide the conversational AI services through voice over call center, AI speakers, and multilingual conversation. We are also working on a speech synthesis technology (TTS) to generate the responses in forms of the natural speech.

MoneyBrain Team Members

CEO / Development Executive
  • Seoul National University, School of Electrical Engineering
  • SNU Student Venture Network
  • PageOn CEO
  • SK C&C Solution Development Team
  • Finger Research Director
  • Pierre
    Deep Learning | Cognitive Science
  • Ph.D in Brain & Cognitive Sciences, SNU, Korea
  • D.E.A. (Master), INSA Lyon, France
  • BS in Computer Science, KAIST, Korea
  • Post-doc Researcher, Aalto University, Finland
  • Visiting Scientist, Max Planck Institute, Germany
  • Marc
    Deep Learning | Neuro Science
  • PhD Candidate in Brain & Cognitive Sciences, Seoul National University
  • MA in Psychology, Seoul National University
  • BE in Electrical Engineering, Seoul National University
  • Visiting Researcher at Vanderbilt University
  • Cofounder of Neurogazer
  • RJ
    CGO / Investment, Finance, Global Business
  • Department of Business Administration, Korea University
  • SK Broadband International Finance Team
  • CDNetworks IR Team
  • Cell Biotech Management Department
  • Jipark
    Data Scientist
  • Master of Urban Planning at Seoul National University
  • Bachelor of Statistics at the London School of Economics
  • SJ
    CMO / Planning and Marketing
  • Master of Design Management, Hongik University
  • Innotion digital marketing
  • SK Communications Planning Team
  • Samsung Semiconductor Research Institute
  • 赵爽(Chinese)
    Chatbot Service Development
  • Master of Electrical Information Engineering, Seoul National University
  • Bachelor of Shandong University (山东大学)
  • Harry
    Hardware Development
  • KAIST Electrical and Electronics Engineering Department
  • SK Hynix DRAM Development Division
  • Ray
    Chatbot Platform Development
  • Kwangwoon University Computer Software Engineering
  • SK Corporation C&C R&D Institute
  • Solution / Convergence Technology Development Team
  • Cloud Platform Team
  • Kim
    Sales Executive
  • Department of Computer Engineering, Pukyong National University
  • C&D representative
  • LG U + Consulting Team
  • CJ Hello Vision Convergence Technology Team
  • Jun
    Chatbot Platform Development
  • Life Sciences at Seoul National University
  • SNU Student Venture Network
  • ByheyDay developer
  • Lisa
    Design / Publishing
  • Sangmyung University Western Painting Master
  • CoreBank Technology Institute
  • UI / UX Design Development Team
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    585 Broad way St.Redwood City,CA 94063
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