SDLC amplifAI: Transforming software development with AI-driven efficiency and speed

Customer Challenge:

• Poor software quality including costs from operational failures, unsuccessful projects, and software errors in legacy systems multiplies the cost
• As per the study Developers spent an average of 20% of their timing fixing software bugs
• Inter-team dependencies
• Lack of clarity of the actual requirement
• Unavailability or Lack of subject matter/ domain expertise adversely impacts the project

Introducing SDLC amplifAIer:

Tech Mahindra’s SDLC amplifAIer is a cutting-edge solution that enhances the traditional software development life-cycle by integrating the power of Azure AI services. This innovative approach utilizes AI algorithms to automate key tasks, improve decision making and accelerate the software development process, delivering high quality software with greater speed and efficiency. The need of SDLC amplifAIer solution arises from the challenges associated with the traditional SDLC, which involves lot of pain sticking documentation and efforts from associates in different roles. The solution has been developed to boost efficiency and accuracy to make it a valuable asset in the modern software development landscape.

Our Advisory & Consulting Services:

1. Customized GenAI Studio
2. Enterprise knowledge Search
3. Data augmentation for models
4. Responsible AI adoption
5. Document summarization & reviews
6. Domain amplifAI
7. Document amplifAI
8. Code amplifAI
9. Quality amplifAI
10. Document amplifAI
11. Modernization & Maintenance

Possible Scenarios:

• Document QnA
• Document Summarization
• Agile Artefacts Generation
• Code Generation, Understanding
• Test Artefact Generation
• RCA and Data Analysis
• Domain Understanding

Business Benefits:

• Faster turnaround time due to GenAI’s ability to consume data and provide relevant information
• Quick content output (e.g., documents, codes, test cases) due to its content generation capability
• Relevant information and quicker content generation leading to Time and Effort Saving
• Expected to improve the overall program throughput by 35% to 40%