Companies have complex and unique challenges, which depend on specific data science algorithms and models. It will use data from internal and external sources that are possibly not integrated or sanitized. At SciCrop, we create on-demand algorithms that have solved a number of open innovation challenges for our customers.

Analytics modules only make sense when people use and consult the results generated for decision making. Therefore, we offer at SciCrop personalized data visualization as dashboards for computers and mobile devices, maps for geoanalytics systems and virtual assistants based on chatbots. The use of chatbots has been very effective in the field.

When a company starts the data-driven mindset, naturally the innovation process starts to happen. And the opposite is also true. However, it brings questions on how to manage vertical and horizontal innovations, as well as, challenges to collect and process data that will be the main inputs of this innovation journey. SciCrop has become a partner in our clients’ innovation journey, helping in creation of the innovation master plan, committees and assessments.

There is no analytics without data. The lack of availability and reliable data in a company is one of the biggest obstacles of a successful path in the data science and artificial intelligence world. However. it does not mean that data does not exist, it may be distributed around several systems that do not communicate within each other. Therefore, it is fundamental to integrate data in a single api bus that allows future new systems to be deployed under the same architecture. SciCrop offers an expertise to unify data that will be essential for analytics, under the same protocol.

Companies working with agribusiness or related sectors are transmitting and receiving a huge amount of data that are fundamental for their business. However, these data are not being stored unified, which causes difficulty to correlate and use them for analyzes that may bring real strategic insights to these companies. Therefore, the awareness for technological infrastructure focused on data and its support has grown more and more, whether for data warehouses, data lakes, MLOps or DevOps environments. SciCrop supports this initiative inside its customers and helps to accelerate the adoption and implementation of this type of infrastructure.

The autonomy in analytics, big data and artificial intelligence depends on two main assets: human and technological resources (or algorithms and infrastructure). When a business proposes to be data-driven, more than technology providers, it needs to have an internal knowledge base and people capable of deciding and growing on its own data-driven journey. To promote this autonomous growth inside our customers, SciCrop offers all the support through knowledge transfer, not only co-developing sophisticated new algorithms but also training internal professionals so that the company can walk alone from now on.