Building Solution Provider Gains Operational Efficiency With .NET
About
Over the course of more than half a century, the client has been crafting tales of accomplishment alongside designers, builders, and developers just like you. This impressive past history is the result of a lot of hard work and our mindset that things should be done well right from the start. We have vast experience in developing new structural and architectural construction solutions, and we are honored to be affiliated with a few popular building systems in the United States.
Project Highlights
In partnership with Microsoft, Brainvire has developed a comprehensive application that empowers companies to streamline their day-to-day business operations. This innovative solution facilitates various tasks, including company creation, contact details management, project and job additions, estimations, task assignments, and the efficient tracking of critical data like timesheets and expenses.
The Challenges
- Run Multiple Functions Simultaneously:The client had to experience a tedious process of encoding and decoding each function individually.
- Poor Loading Of Data:The client struggled to load its massive business data and task details due to server issues.
- Effective Third-Party Integrations:The client required additional functionalities to enhance their operations within the firm.
- Difficulty in tracking:Tracking was difficult as data was huge and it was scattered along.
Tech Stack
jQuery
CSS 3
Microsoft SQL Server
.Net core
Bootstrap
HTML
Result
Helped Save Time
We noticed that the client wasted several precious business hours in encoding and decoding functions. Hence, we created a custom attribute that helped run operations rapidly.Quicker Data Access
With the implementation of SOA, the client had better access to business data and could use it efficiently to make lucrative business decisions in a hassle-free manner.Improved Operational Efficiency
Increased Scalability: Data marts and data cubes allow you to scale the amount of data you can store and analyze by partitioning large tables and by using aggregation.Increased Scalability:
Data marts and data cubes allow you to scale the amount of data you can store and analyze by partitioning large tables and by using aggregation.
