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Open-Source Intelligence (OSINT) is usually defined as intelligence produced by collecting, evaluating, and analyzing publicly available information to answer a specific intelligence question.  Here are some common ways in which OSINT is used today: Security and Intelligence:  Business and Market Research Investigative Journalism Academic Research Legal Proceedings It is an exceptional tool for gathering information on a wide range of topics such as security threats, market research, and competitive intelligence, and can also be used by a variety of organizations and individuals to inform decision-making and strategy.   Challenge OSINT is transparent and can be easily verified. This means that organizations can be confident in the accuracy and reliability of the information they gather. It also offers many advantages over other forms of intelligence collection, making it a valuable tool for a wide range of organizations and individuals. The client approached us with the challenge: develop an OSINT (open source intelligence) tool, which aims to collect, consolidate, and make a detailed accurate analysis of personal data and user-friendly presentations. Solution To build and develop an OSINT tool, we proposed and did the following: Data mining from third-party providers (Social media, trade registers, search engines). Data collection, processing, and consolidation. Data indexed and searchable. Machine learning analysis. Event-driven architecture on AWS. What technologies did we use to make this a reality? Our team consisting of a Tech Lead, Project Manager, Data Engineers,  ML Developers,  FE Engineers, and a Quality Assurance Engineer used Machine learning, Python (Starlette Framework), TypeScript (ReactJS), AuroraDB, ElasticSearch, DynamoDB, Docker and worked with AWS Services for Containerization, Messaging, Storage. Results The results of our ready-to-use solution are looking encouraging. We successfully managed to deliver the following: An OSINT tool that is production ready. The client has already onboarded the first customer. The solution also gives our client the advantage to fast delivery reports. The machine learning prediction success rate amounts to +91%.

Even today we still have many examples and situations where labs rely on their manual and paper-based processes. However, in recent times, modern lab managers are becoming more aware of the benefits that digitization can bring to their operations.  It is more evident than ever that with the support of new and smart technologies, previous efficiency- and productivity-related challenges are beginning to dissipate as manual processes are starting to be replaced with automated and integrated applications, helping to pave the way toward a fully digitalized lab. What are the benefits of data digitization? Firstly, digitization improves data management as it provides a centralized place for storing, accessing, and analyzing data. This allows for efficient data sharing and collaboration among lab operators.  Additionally, digitization enhances laboratory efficiencies by automating manual tasks and improving workflows. Moreover, digitization enables real-time monitoring and analysis, providing valuable data insights and enabling proactive decision-making, which leads to better quality control, optimized resource utilization, and reduced costs.  Furthermore, digitization enhances compliance and traceability by maintaining comprehensive records and audit trails. Challenge Our client had an idea and approached us with the challenge: they wanted a customer-centric solution that makes manual data management in the lab obsolete and gives lab technicians and quality managers more time to focus on what really matters: working efficiently with the data. They needed the following:   Processing and organizing industrial data in one centralized place, a platform where lab operators can easily analyze, combine and compare different tests and measurements. Project management of testing various materials  The ability to custom data search and create custom reports And laboratory management What technologies did we use to make this a reality? Our team worked with ReactJs, Redux, and Axios as front-end technologies used NestJS, NodeJS, REST API, MongoDB as NoSQL as back-end technologies, and lastly, for ETL, they used Python, AWS Technologies such as S3, EC2, and CloudFront, SQS, SES, Load Balancer, Lambdas, and Cloud Watch. Results Following a close collaboration with our client, we delivered a solution that intelligently links test equipment and measurement data and improves the workflow in your laboratory with fast integration, reliable project management, and outstanding interpretation.  The new software lets material testing labs facilitate collaboration, get more data insights, establish consistent workflows, and enhance lab efficiency.  The new data insights feature gives them the option to create a custom table using the test results, modify the same using various statistical operations, chart the data from the custom tables, and use the final customization in the Word report.