Financial Advisory Firm Accelerates Software Delivery with Claude Code and Agentic AI

A financial advisory firm needed a secure, web-based platform for advisors to analyze and compare complex compensation data and deliver faster, better-informed client recommendations.

SPR delivered a production application in approximately four weeks using Claude Code, Agentic AI workflows, and experienced engineering oversight. The project demonstrated how AI can accelerate far more than coding, helping compress requirements gathering, backlog creation, architecture design, testing, and deployment into a significantly shorter delivery cycle.

“The biggest surprise wasn’t how quickly Claude Code generated code. It was how quickly we moved from conversations to a deployed application. Activities that traditionally happen across multiple roles were compressed into a single workflow while maintaining engineering oversight.”
Mike Saccotelli, SPR

The client needed a real application, not a prototype.

The platform needed to be secure, deployable, and capable of evolving as business needs changed.

The challenge was transforming stakeholder conversations into requirements, requirements into a backlog, and that backlog into architecture, tested software, and deployed infrastructure.

Traditionally, that process involves multiple roles, including product owners, business analysts, developers, testers, architects, and cloud engineers. Each handoff introduces additional time and coordination.

This engagement was well suited for an AI-driven approach. The project was greenfield, the business domain was clearly defined, and there were no legacy systems creating additional complexity.

SPR ran the full delivery lifecycle through a human-guided workflow centered on Claude Code.

Discovery started with stakeholder sessions. AI-assisted transcription and analysis turned those conversations into structured requirements, which were refined into roughly 100 Jira epics and stories within days, a process that often consumes weeks of workshops and backlog grooming.

Model Context Protocol (MCP) integrations moved work directly between Claude Code and Jira, creating a streamlined path from requirements to implementation.

From the backlog, Claude Code helped generate:

• Application architecture
• Data models and schemas
• React front-end components
• .NET backend services
• APIs and business logic
• Automated tests
• Realistic seed data
• Container configurations
• Azure deployment assets

One experienced SPR engineer guided the entire process.

Rather than spending time manually creating every artifact, the engineer focused on directing the workflow, validating outputs, making architectural decisions, and ensuring alignment with business requirements.

The engagement demonstrated that activities traditionally performed by product owners, business analysts, developers, and testers can be consolidated into a single AI-augmented workflow under experienced engineering oversight.

AI accelerated the work, and engineers remained responsible for every significant decision.

Requirements, architecture, source code, tests, authentication flows, deployment configurations, and infrastructure assets were reviewed before being incorporated into the solution.

Reviews focused on:

• Maintainability
• Architectural consistency
• Code quality
• Security of data handling and access control
• Alignment with business requirements
• Long-term supportability

Like any development process, AI-generated outputs occasionally introduced redundancy or drifted from the intended architecture. Those issues were identified and corrected during review.

That review process was a critical part of the engagement. The objective was to generate software faster and to deliver software that could be confidently deployed, maintained, and evolved over time.

Where appropriate, automated quality and security analysis can be layered into the delivery process to further validate generated code before deployment.

The final solution was deployed to Microsoft Azure and included:

• React front-end application
• .NET backend services
• Authentication and user management
• Business APIs
• Database schemas and seed data
• Automated tests
• Containerized deployment
• Supporting cloud infrastructure

Technology

• Claude Code
• Model Context Protocol (MCP)
• React
• .NET
• Microsoft Azure

Approximately 100 Jira Work Items Created in Days

This accelerated a process that often requires weeks of meetings, workshops, and backlog refinement.

Production Application Delivered in Approximately Four Weeks

The engagement included requirements generation, backlog creation, architecture design, development, testing, deployment, and infrastructure configuration.

Roughly 300 Story Points of Functionality

Using conventional Agile estimation, a common industry heuristic rather than a precise measure, that scope equates to roughly 150 developer-days of traditional output.

That estimate does not include the additional effort typically associated with product ownership, business analysis, quality assurance, project management, cloud engineering, and deployment activities.

The engagement demonstrated how a single engineer, augmented by Claude Code and Agentic AI workflows, could perform activities traditionally distributed across multiple delivery roles.

Most discussions about AI in software development focus on code generation.

This engagement validated that the larger opportunity extends beyond writing code faster.

By using Claude Code throughout the delivery lifecycle, SPR accelerated requirements gathering, backlog creation, architecture design, implementation, testing, and deployment.

The project demonstrated that activities traditionally performed by product owners, business analysts, developers, and testers can be consolidated into a unified AI-augmented workflow while maintaining engineering governance and accountability.

Perhaps most importantly, the engagement validated that experienced engineers can remain focused on architecture, quality, security, and business outcomes while AI accelerates the creation of software artifacts.

For organizations evaluating AI, the opportunity is developer productivity and compressing the time required to move from an idea to production-ready software.