Raj Information Systems’s application migration and reengineering services help our clients transition their mission critical application and productions from legacy platforms to newer, state-of-the art technologies. Using our proven methodology, best practices and tools, we successfully migrate and reengineer customer legacy applications. We add value by providing contemporary, web- based graphical user interface for modernizing legacy with our in-depth knowledge of emerging technologies and third party tools. Our clients take advantage of our highly skilled offshore software development team at cost efficient rates.
By migrating your legacy products from older legacy technologies, to new state-of-the-art platforms, your custom software products will benefit from improved integration capabilities, faster acceptance in the market and increased scalability.
Raj Information Systems’s migration services portfolio includes:
- ASP to ASP.NET
- Client Server to N-TIER
- Legacy to Web Services
- Client Server to Web Application
- Legacy to Web Enablement
- Microsoft Access
Raj Information Systems helps you reevaluate the needs of your customers, re-architect your product according to those requirements and develop it using new, modern technologies that will enhance your customers’ experience. Turn to Raj Information Systems’s engineers for an expert “tune-up” your product—helping maximize performance while improving stability and usability.
Raj Information Systems Advantage
- Proven and well defined methodology and best practices in migration and re-engineering
- Emphasis on Tool based approach to significantly reduce the effort and the cost. Further effort reduction is achieved by utilizing the components from Raj Information Systems Re-usable Components Library.
- Modern and Consistent User Interface using the third party tools like Infragistics, SyncFusion, Teleriks
- Qualified and Experienced programmers and managers with migration tools and process expertize
- Iterative — module by module — approach where the lessons learned from one iteration are applied to the next.