Bambino.ai AI E-health App | Edge One Solutions Case Study

Development of the Bambino.ai E-health App for Fertility Monitoring

bambino.ai

Project overview

Client: Bambino.aiSector: e-health / FemTechModel:IT outsourcingArea: AI / fertility / e-health app

Edge One Solutions supported Bambino.ai by providing IT specialists responsible for developing an e-health app supporting menstrual cycle and fertility monitoring.

The project involved the use of AI/ML algorithms, analysis of data related to skin electrical resistance, and development of a solution supporting more precise identification of fertile days and ovulation.


Tools and technologies used

  • TensorFlow
  • Sickit-learn
  • PyTorch
  • Apache Kafka
  • MongoDB
  • Docker
  • Challenge

    The key challenge was to develop a method that could support more precise ovulation prediction without interfering with the user’s natural biological rhythm.

    The solution had to take into account individual differences in skin electrical resistance and changes occurring during the menstrual cycle.

    It was also important to use clinical study data correlated with blood test and ultrasound results so that the algorithm could better support the analysis of fertile days and ovulation.

    bambino.ai
  • Scope of work by Edge One Solutions

    Edge One Solutions supported Bambino.ai by providing IT specialists responsible for developing a solution based on artificial intelligence and data analysis.

    The scope of support included work on algorithms supporting menstrual cycle, fertile days, and ovulation analysis, taking into account biological data and clinical study results.

    The team also supported solution architecture, data processing, and the technological foundation for further development of the application and related measurement devices.

    bambino.ai
  • Solution

    As part of the project, the Bambino.ai app was developed using artificial intelligence algorithms to support fertility and menstrual cycle monitoring.

    The solution analyzes data related to individual differences in skin electrical resistance and changes occurring during the user’s cycle.

    The project also created a technological foundation for further development of the Bambino.ai ecosystem, including work on a proprietary band for measuring skin electrical resistance and product expansion into international markets.

  • Project significance for e-health and FemTech

    E-health and FemTech solutions require a combination of advanced data analysis, responsible product design, and strong sensitivity to the health context of users.

    The Bambino.ai project shows how AI/ML algorithms can support biological data analysis and help create tools for fertility and menstrual cycle monitoring.

    For companies developing e-health products, similar solutions can support user experience personalization, digital health service development, and building a technological foundation for product expansion.

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