“We were amazed at the speed at which they could create, delete and update instances to meet new requirements. The affordability of Cloud Platform and Google Apps for Work made these tools the right combination for the software development team, as well as the business team."
"The ability of Google Cloud products to deliver insights on how people are using our platform, and what they want from it, is extremely useful for helping us to enhance the customer experience. This, together with the rich location data that Google Maps Platform provides, has been the recipe to …
“The idea behind SoCash is to use technology to remove banking from branches and take it directly to the consumers.”
"Because our business model is not based on small profits and high sales volumes, it is important to secure gross profits. However, the development of AIMD was not an easy task. Before adopting Google Cloud, we worked with several vendors for about three years. Although we worked on development, we …
"Since AIMD is built with serverless products on Google Cloud, there are no operational burdens such as changing specifications or applying patches. The throughput of Vertex AI's AutoML is also higher than expected, so we are considering increasing the number of products to be forecasted. We have expanded our scope …
“We were moving to a model of mobility as a service. We needed to reshape the way we use technology and data to better serve our partners and customers. We feel that Google Cloud has the most powerful toolset and vision to meet the needs of our strategy.”
“We integrate digital networks for our customers globally. As we continue to drive deeper customer engagement and expand into new geographies, data-driven decision-making becomes critical. STL’s data lake, built in Google Cloud, empowers us to do that by securely managing and analyzing large volumes of diverse business data. It has …
“Our business is all about data networks. With the help of BigQuery, we can use data internally to make the right decisions or even to make timely surgical interventions, be it around the efficiency of our manufacturing processes or the predictive models for our quantitative and qualitative performance metrics.”
“Our architectural landscape and transformation design principles enabled us to avoid running separate data warehouses. Creating a data lake enabled us to extract deep insights from historical and real-time data from disparate sources in a unified, clean format. This is a great benefit and big time saver for us.”
"Our trigger for change came from the strategy. Continuing with a traditional hosted service wasn't sustainable in view of the growth we were expecting, so we knew we had to move into the cloud. We saw that as an opportunity to transform our architecture."
“We needed a better way to grow with our data.”
"We envisioned that all software will be written with the use of AI in the near future, whether it was in creating software, in reviewing it or deploying it, and probably all three. When we started this sounded like a dream but now, it is an everyday reality."
"We currently have more than 150 machine learning (ML) models and are adding more as the complexity of our product grows and we add more features for customers With Google Kubernetes Engine and Cloud Run, each ML model can operate independently, allowing greater flexibility for the team."
“We wanted to provide enterprise reliability and quality at prices smaller businesses can afford.”
"We were looking for security to go for a full platform deployment, minimizing risks. We chose Google Cloud due to network traffic requirements, one of the cloud’s strengths. As for mass data processing, Google Cloud’s team helped us with the project and supported everything tech-related."
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