How a financial services firm saved $900K via cloud modernization

With an imminent deadline looming, the company had to act fast to move critical workloads off its old and vulnerable platforms to avoid penalties. That said, the company’s core applications had grown up on legacy on-premises infrastructure, with several databases sitting at the center of a dense web of systems and services. This made changes risky and slow, as a misstep could shut down their business operations for hours or longer. The company also lacked the necessary capacity and expertise in current cloud migration strategies and secure methods for using AI to speed up code transformation.

The company’s core databases were deeply intertwined with dozens of upstream and downstream services. This complexity made changes tricky and exposed the organization to compliance penalties if modernization lagged.

While the company’s internal teams were strong, they lacked hands-on leadership and expertise in cloud migration and safe AI adoption.

With the regulatory deadline looming, there was little room for error. The company also needed to migrate systems without shutting down the business for prolonged periods.

Instead of scaling a large staffing model, the team proposed a lean, blended onshore and offshore team that concentrated expertise where it mattered most. Kforce also conducted a comprehensive assessment that combined executive interviews, technical reviews and readiness workshops.

Throughout, Kforce made sure to integrate with the client’s operating model and needs. The consulting team focused on technical leadership, patterns and execution accelerators, and project management remained with the client.

Used an AI code-conversion tool to accelerate upgrades across 8,000+ lines of legacy Java code, reducing error rates and achieving up to 10x speed-up versus manual conversion.

Sequenced database moves and service refactors to close the most material risks first, embedding security and auditability from the start.

Implemented CI/CD workflows, automated testing and coached engineers on secure, responsible use of AI tools. This greatly increased delivery speed while maintaining quality.

Applied FinOps principles were used to model and manage cloud costs, create right-size environments and avoid lift-and-shift waste during cloud migration.

Built a blended onshore and offshore team of experts for cost-effectiveness and rapid execution.

Within 9 months, the team created several key deliverables for the company:

• Modernization roadmap and migration playbooks that prioritize high-risk systems, map dependencies and codify rollback strategies.
• Cloud reference architecture and security baselines tailored to the company’s controls environment.
• AI-assisted code conversion pipelines with guidance on when to trust, review or reject automated changes.
• An SDLC uplift package that includes CI/CD workflows, automated testing harnesses and quality gates aligned to compliance needs.
• A FinOps toolkit with environment right-sizing, budget guardrails and cost visibility dashboards for product owners.
• Knowledge transfer sessions and documentation so the company’s engineers can sustain and extend the new ways of working without heavy external support.

The combination of compliance-first planning, AI acceleration and process uplift delivered significant results:

The modernization process was completed within nine months, significantly ahead of the projected timeline of three years. Additionally, there was a tenfold increase in code conversion speed for over 8,000 lines of code.

The team achieved $900K in savings by optimizing team design, leveraging AI-assisted conversion and applying disciplined cloud cost management. This work eliminated the need to hire five or six more Java engineers for manual conversion tasks.

Critical databases and services were migrated to cloud, closing significant exposure risks. Audit-ready artifacts and security controls were validated as well.

Engineers now have a clear blueprint for implementing secure AI practices, CI/CD and automated testing. This approach has also reduced technical debt and improved the organization's ability to adapt to future changes.

Playbooks, roles and processes are easier for new teams to learn and use, enabling faster onboarding and consistent delivery.