Projects

The UAE-based healthcare products distributor focused on optimizing its data management. By centralizing its siloed data in data warehouses and performing data cleaning and ETL processes, the project achieved streamlined operations. Leveraging Google Cloud's DataPrep, BigQuery, and DataStudio, the company successfully transformed raw data into actionable insights. This comprehensive approach enhances decision-making processes and provides valuable business intelligence for the distributor's operations in the healthcare sector.

A Property Valuation Management Platform in Georgia successfully migrated from a traditional hosting to a secured, scalable serverless architecture. This transition was facilitated using Google Cloud's managed and serverless services : Cloud Run, PubSub, and Function services. By leveraging these tools, the project achieved improved scalability, reduced operational complexity, and enhanced overall performance, ensuring a more efficient property valuation process.

The French online retirement homes directory underwent a migration from an on-premises monolithic setup to a serverless architecture. This transformation involved employing Google Cloud's Cloud Run (through a "dockerization" of services), serverless Function, Cloud Armor for web application firewall, Cloud Build, including a vulnerability scanner. The project's shift ensures enhanced scalability, security, and efficiency, offering users a streamlined and responsive experience and also a modern development experience for its technical team.

The US Peer Support Network app facilitates moderated emotional support for healthcare workers facing high stress, trauma, and burnout. It's an anonymous, confidential, and moderated space that fosters connections through discussion boards, voice messages, and live calls, helping caregivers find support and community. The project is purely serverless to ensure scalability using Firebase with serverless functions for its backend operations. Leveraging BigQuery (serverless data warehouse), and Data Studio, the project effectively gathers and analyzes valuable insights from user interactions, enhancing its functionality and user experience.

By harnessing the critical capabilities of Google Cloud services, Olmait collaborated with an open-source intelligence organization to develop a cutting-edge data platform , that empowers open-source intelligence researchers to effectively gather, monitor and analyze data from diverse social media platforms and other data sources. Olmait utilized mostly cloud native services such Cloud Functions, Pub/Sub, and Cloud Storage among other Google Clouds services to successfully collect and process the desired data and secured the data leveraging most of security tools from Google Cloud (Web Application Firewall with Cloud Armor, Cloud NAT, Vulnerability scanner, …). Thanks to this robust solution, the OSINT researchers were empowered to uncover insights that would have been otherwise unattainable.

The UK-based Marketplace for Trip Organizers, with the help of Olmait, employed an event-driven serverless architecture to seamlessly connect diverse SaaS and APIs. By utilizing Google Cloud's Pub/Sub, Function, and DataStore services, the project enabled efficient communication and integration among different components. This approach enhances the platform's agility, scalability, cost-efficiency and responsiveness, providing a user-friendly experience for both trip organizers and participants in the dynamic travel marketplace.

The architecture of the Sensor-Kinesis product, supported by Olmait technology, will allow patients to perform tests remotely (possibly at a temporary testing facility) and share results instantaneously with their primary care provider for diagnosis and treatment prescription. The revolutionary testing kit can be used not only for COVID19 but also cardiac life-threatening conditions, as well as various other physiological diseases. The cheap, user-friendly new device would yield results in a matter of minutes and would enable to mass-test millions of people around the world.

The mobile app enables the maternity department staff and breastfeeding consultants to support the mothers in the months after returning home. This ongoing connection is expected to increase breastfeeding duration and reduce postpartum depression. A survey system was set up to collect data from the participants, as part of a research conducted by the hospital. We are always proud to build products that make the world a better place. In the media (Hebrew): https://www.mako.co.il/women-women_news/Article-8cc90f28896fe61026.htm

Created and designed a question-answer system to sell products to an insurance company’s customers. Worked on intent detection and trained a custom model to develop an incredibly smart chatbot storing statistics on each user. Used the intend detection algorithm implementation, Python, Flask, MongoDB and - last but not least - React for the admin panel.

Using Microsoft’s one million celebrity face open dataset to train the Neural Network, creating a full-proof face recognition and user identification search. The face identification software created has more than 90% accuracy. Used Convolutional neural networks, Recurrent Neural Networks, trained a set preparation from MS-Celeb-1M, worked on Neural Networks - PyTorch, and Computer Vision - OpenCV.

Developed a neural network to predict car damage from an image, what the car make is, and determine from which point of view the picture was taken. By collecting a major training set and label it correctly, created a state-of-the-art model for predictions. The insurance company then used the Neural Network model to create an app for its users, enabling them to insure their cars without any help of an insurance agent. Used Convolutional neural networks, trained a set preparation from various sources, and worked with Tensorflow, OpenCV, Scikit-learn, and Scrapy.