blackthorn.ai

Our company recently had the opportunity to develop a billing system software for a large media streaming provider. Our team worked closely with the client to understand their specific needs and requirements for the software. One of the significant challenges we faced was integrating the software with their existing electronic record system. We worked closely with their IT team to ensure a seamless integration that would maintain their daily operations. Another challenge we faced was ensuring the security and confidentiality of patient information. We implemented strict security measures and encryption protocols to protect sensitive data and prevent unauthorized access. To ensure the software was user-friendly and efficient, we conducted multiple rounds of testing. We gathered feedback from the client’s billing team. We made necessary modifications and improvements to the software based on their feedback to ensure a smooth and efficient billing process. The result was a robust and user-friendly billing system software that met the client’s specific needs and requirements. The software has dramatically improved the efficiency and accuracy of their billing process, saving them time and money in the long run. We are proud to have developed a solution that has positively impacted our client’s operations.

A novel graph neural network architecture was designed for organic reaction yield prediction. The network combines structural information, molecular-, and reaction-level descriptors. Our approach outperforms or equals all known AI models predicting chemical reaction yields.

blackthorn.ai delivered three projects for a clinical center:The first project analyzes and identifies several markers, from patient data to predictive outcomes.The second task was to build a data bank to use as a foundation to integrate patient data further.The third effort was to provide a frontend application to visualize the data integrated into that data bank.

Customer is a streaming platform that offers a wide variety of TV shows, movies, documentaries, and more on thousands of internet-connected devices.SolutionsTotal Cost of Ownership (TCO) was carefully considered by comparing the performance and costs of alternative services offered by a number of cloud providers. The most advantageous combination of cost, quality, and sustainability was chosen to meet customer requirements.Business requests were collected by interviewing numerous stakeholders. The requests served as a starting point for the BI dashboards, the DWH schema, and data pipelines design.The multidimensional OLAP cube (facts, dimensions, etc.) was built and extended piece-by-piece revealing one business request after another. The cloud warehouse consolidated large amounts of data from disparate on-premises sources.Convenient BI dashboards were designed and implemented to meet collected earlier business requests and their possible future variability. The end business users with no SQL skills could perform requests using straightforward BI dashboards, without DBA team involvement.Auto-scalable ETL workflows were designed and implemented as the business requests and DWH schema had been established. The pipelines extract, transform, and load data from the source databases (on-premises) into the target DWH (on the cloud).Backward historical consistency was achieved by introducing meta-tables and unique timestamps that indicated the time range when a source row or table was actual.ImpactUsing simple controls, business users can configure interactive dashboards and get required information immediately and independently, without the involvement of other departments.DBAs are now free from writing the exact same SQLs every day. Finally, they’ve got time to clean up and refactor on-premises data sources. Without fear of breaking analytics - because the cloud DWH securely stores all past states of the originals.