About the ClientBased on its accumulated experience, Geomotiv founded its product subsidiary Adoppler (www.adoppler.com) in 2017 in order to provide faster time-to-market for the companies in the TV sector through the use of programmatic technologies. Adoppler was tasked with developing a full-stack programmatic AdTech platform (Trusted Marketplace) that would facilitate the media buying and selling process.GoalDevelop a programmatic platform that would help clients to avoid legacy approaches and take into account both sides of the media buying process in a secure and transparent environment for AdTech transactions based on the Trusted Marketplace technology.Work DescriptionAs a first step, the platform became an RTB exchange that could interact with programmatic buyers and sellers based on the OpenRTB protocol. Later on, SSP capabilities were added. To achieve the possibility to conduct direct deals through a programmatic pipe, the Adoppler team built support for PMP and PG deals. Then, DSP capabilities were added. At this stage, the Trusted Marketplace made it possible to manage advertisers, their campaigns, and creatives. And finally, Adoppler  developed an advanced reporting tool that added a full-stack AdTech functionality to the system. ResultsA horizontally scalable system was developed and then tested in production with peak loads up to 20K QPS. The work was accomplished on time in compliance with all requirements declared and industry standards involved. As a result, the first version of the product was successfully introduced into international markets.

About the ClientOur client was a pioneer in a niche business segment related to digital-to-direct mail. They created a unique marketing platform with over 100 brand partners and a listing in ARF’s First Innovators A-List.GoalThe client needed to rebuild their existing platform entirely as the business grew rapidly and the company needed a more scalable solution. The current platform was developed as a proof of concept and wasn’t designed for huge loads. In addition, the client wanted to utilize Big Data technologies to the project to ensure high performance and scalability.Work DescriptionThe client’s original solution was based on Python which didn’t provide enough scalability for the rising demand. The client wanted to rebuild the platform completely and implement new web features. At first, Geomotiv ran deep technological analysis and provided a new architecture plan. Our team decided on Big Data technologies such as Scala, Spark, and GraphX for the solution. The client didn’t stop the original project, so we were supporting it and creating another one at the same time. Later on we performed full data migration from the primary solution to the new one and then the client stopped the original project. Our web technologies stack comprised Java and Spring for back-end and React for front-end. Our client had an agile (scrum) development approach and we were essentially an extension of the core team. The management process included three-week sprints, scrum meetings, backlog refinement, sprint planning, etc.ResultsHaving partnered with Geomotiv, the client was able to rebuild their entire system to the new business requirements. A specialized Big Data stack helped ensure scalability and fault tolerance of the delivered solution, which original Python-based platform couldn’t provide. With that system, the client managed to reduce the costs of additional support and provide their users with a greater scope of services.

About the ClientThe client came up with an idea to develop a medical chatbot that could assist doctors and nurses to improve patients’ health, prevent indeterminate diagnosis and medication, and improve medical services. GoalOur aim was to create a chatbot able to provide the patient with relevant information based on their health records. The client planned to cooperate with three types of organisations such as health insurance offices, hospitals, and pharmaceutical companies, so the solution had to comply with their businesses as well.Work DescriptionThe team had experience in building similar solutions, so they had a ready strategy. The engineers decided to build the app on the basis of AI and ML due to the specific requests by patients. The answers were provided based on the daily updated knowledge base, patients’ health records, and medical questions history.Procure business requirementsCreate the architectureDevelop back and front endsRefactor the codeProvide Quality AssuranceDeploy in productionResultsTogether with the client, we managed to develop the entire project on time to help them continue the fundraising campaign for their startup.