AI-based app design software

  • Industry

    HR & Recruitment

  • Location


  • Platform


  • Cooperation

    3+ years

  • About the project

    Our client,, provides explainable AI solutions for enterprises across a wide range of industries. Their platform-as-a-service is used for process control, ultility management, anti-corruption & fraud, risk management, and other use cases.

    The Solution helps users create simulations and automatically predict outcomes. They can develop and dynamically update applications through a simple and easy-to-use web interface. It is valued by our numerous clients – Compsis, Con Edison, Constellation Energy, CPower, Federal Energy Regulatory Commission (FERC), North American Electric Reliability Corporation, to name just a few.



    • To make the creation and managing of a cognitive map easy for the user on different devices.
    • To make application architecture scalable and flexible for implementation of new features/different users’ requirements/systems.
    • To make the creation and management of the decision table easy and clear for the user.


    What helped us achieve the project goals: best practices for mind maps, user interfaces, and continuous integration; the usage of flexible app architecture solutions, development tools, and methodologies, rich preferences for customer personalization; best design practices for managing huge amounts of structured data.

    Cognitive map

    The creation and management of Cognitive Maps is a highly interactive task for users. Cognitive Maps evolve through feedback from visualizations that highlight relationships between goals and situations, show where the map needs to be expanded, changed, or reduced. Solution is designed to enable interactive creation with intuitive displays and ease-of-use features.

    The Bayesian Network

    The Bayesian Network (BN) user interface offers the following possibilities:

    • Manual creation of BNs by creating Nodes and Links and entering BN Node/Link data.
    • Automatic learning of a BN structure from user-supplied data (via CRex API).
    • Automatic learning of BN node probability tables from user-supplied data (vis CRex API).
    • Manual modification of the BN node structure, parameters (states, probability tables), and connections. In a Decision Table, use evaluation of a valid BN (via CRex API) to assess part of a Condition Clause and display the results of the evaluation.

    Simulation Capabilities

    When developing Cognitive Maps, users often simulate a running application so that they can examine the results and modify the Cognitive Map to achieve desired reasoning behavior. To do this, they use the Simulation capabilities that allow the user to do the following for a single complete and publishable Cognitive Map:

    • Manual crea.
    • Connect to a Microsoft SQL database containing data that are the inputs to a CRex application of BNs by creating Nodes and Links and entering BN Node/Link data.
    • Edit the input data.
    • Visualize the results of the simulation in an embedded Microsoft Power BI display.


    React js


    Styled components

    .NET Core

    SQL Server

    Entity Framework

    Business value

    The solution merges the cognitive reasoning power of artificial intelligence, predictive analysis, and the performance of Big Data and cloud computing environments. The customers extensively use it for building their own high-level software across various industries, including gas and oil, finance, and government management.

    Contact us

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      Daryna Chorna

      Customer success manager