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Helping a Finance technology provider reduce software development cost and accelerate delivery through intelligent SDLC automation
The customer is a leading Finance technology provider supporting complex digital commerce operations for a broad range of businesses. With multiple development teams working across products, integrations, and customer-facing capabilities, the organization was looking for ways to improve engineering productivity while reducing the overall cost of software delivery.
The customer wanted to explore how AI could be applied beyond individual developer productivity tools and across the broader Software Development Life Cycle. Intellious designed and implemented an Agentic AI–based SDLC automation framework using multiple specialized AI agents, each responsible for a stage of the development lifecycle and sharing context with the agents downstream — automating activities from requirements analysis and solution design through Jira story creation, coding, code review, and test-case generation.
The customer's development lifecycle involved several manual handoffs between business analysts, architects, developers, project managers, and QA teams, with business requirements manually interpreted into architecture documents, detailed designs, epics, user stories, estimates, code, and test cases at each stage. This introduced inconsistency and significant manual effort. The customer needed to reduce repetitive work, shorten the path from requirements to implementation, and maintain traceability across the lifecycle — without removing human review and governance.
Intellious designed a multi-agent AI framework that moved work across the SDLC — from business requirements through architecture, story creation, coding, code review, and test-case generation - with each agent passing context to the next and human review built in at every stage.
The framework created a more connected, efficient development lifecycle — reducing manual effort across requirements, architecture, development, and testing while accelerating the path from business requirements to development-ready stories. It improved consistency and traceability across BRD, HLD, LLD, Jira stories, code, and test cases, freeing developers and architects to focus on more complex engineering decisions and laying the foundation for lower development cost and faster delivery.
