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    AI-Led SDLC with SoftSpell | Aspire Systems

     Info
    Aspire System’s SoftSpell platform accelerates the end-to-end AI-Led software development lifecycle, covering requirements, design, development, testing, deployment, and modernization, through purpose-built AI agents working alongside with human experts. ReqSpell turns business requirements into structured engineering inputs and surfaces legacy system logic. CodeSpell's agent generates production-ready code, resolves issues, and raises pull requests autonomously. TestSpell generates testcase. Together, these agents cut delivery time by roughly half and speed up feature shipping 3x, all while keeping human reviewers in the loop on every key decision, so automation handles the repetitive work, and people make the calls that matter.

    Overview

    The SDLC Bottleneck

    Enterprise engineering teams lose time at every handoff: requirements get reinterpreted between business and engineering, developers spend more hours reading unfamiliar code than writing new code, and test coverage lags release schedules. Most delivery slowdowns don't come from a lack of skilled people, they come from the manual, repetitive work sitting between each stage of the SDLC.

    SoftSpell - AI Agents Across the Full Lifecycle

    SoftSpell pairs specialized engines with a layer of orchestration agents, so work started in one stage carries cleanly into the next:

    ReqSpell - Requirements Engineering

    Converts business requirements into structured, engineering-ready artifacts

    Surfaces legacy business logic, dependencies, and system knowledge for teams modernizing older systems

    Gives both business and engineering a shared, unambiguous starting point

    CodeSpell - AI-Powered Development

    Agent works autonomously across coding tasks: understanding existing code, implementing changes, resolving issues, and raising pull requests

    Integrates directly with your code repository, so changes flow into your existing workflow rather than a separate tool

    Generates production-ready code rather than boilerplate scaffolding

    Converts Figma designs directly into working front-end code

    TestSpell - AI-Assisted Testing

    Generates test cases based on user stories/API documentation

    Expands coverage without adding manual QA headcount

    When the Agent triggered IT Validates the codebase and make the changes in testcases automatically

    Orchestration, With Humans Still in Control

    The agents pass context to one another across requirements, development, and testing, but nothing ships without a human checkpoint. Engineers and reviewers approve key outputs at each stage, SoftSpell removes the repetitive work.

    Aspire System’s - Delivery Model

    Discover - Requirements intake and legacy system analysis via ReqSpell

    Build - AI-assisted development through CodeSpell's agent, with engineers reviewing and directing output

    Validate - Automated test case generation and coverage expansion via TestSpell

    Deploy - Production readiness checks and release support

    Modernize (where applicable) - Legacy logic extraction and migration planning for teams modernizing existing applications

    Aspire's engineers remain the ones making architectural and quality decisions throughout, SoftSpell accelerates the work between those decisions.

    This service is delivered on AWS, with specific AWS services selected based on each engagement's architecture and requirements, spanning compute, containers, serverless, developer tools, and CI/CD across the AWS portfolio.

    Highlights

    • AI-Led SDLC Automation: ~50% faster SDLC and 3× faster feature delivery and ship cycles with specialized AI agents.
    • Legacy Modernization: Accelerate legacy application understanding and modernization, with ~25% reduction in overall project costs.
    • Design-to-Code & Quality: Transform Figma designs into production-ready code with up to 70% fewer design-to-code defects and ~30–50% lower manual testing effort.

    Details

    Delivery method

    Deployed on AWS
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