Traditional software engineering methods rely heavily on time-consuming manual tasks, often leading to technical debt due to dependencies on individual expertise and institutional knowledge. It is difficult to ensure efficiency, reliability, and scalability across the software development life cycle; these challenges are compounded later in the software life cycle when budget and resource limitations also come into play. Despite the advancements in Generative AI-powered platforms, there is a decline in productivity when managing complex projects that entail interdependencies among applications, a common scenario in complex solutions.
SASVA adopts a distinctive method by constructing a comprehensive knowledge base from vast datasets, including millions of code repositories and documents. Hybrid language models enable efficient utilization, with continual secure training on customer data sourced from various platforms like version control, ticketing systems, project management tools, and collaboration platforms. This rigorous process enhances privacy, security, and regulatory compliance, especially for sensitive data. SASVA streamlines the creation of project plans and generates contextual code modifications for seamless review.
In initial engagements, SASVA has reduced release times by an impressive 25% to 35% with improved quality and reduction in technical debt. SASVA enables clients across industries to boost enterprise productivity across different software phases delivering cost and time efficiency with improved quality.
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