From Legacy Code to Production-Ready Platforms: How Summit Modernizes Federal Analytics

Federal agencies wield decades of analytical power through aging systems—SAS scripts, Excel models, and Stata code—that have quietly driven critical decisions for years. The problem isn’t that these tools ever failed; it’s that they were never designed to scale, adapt, or function effectively in production. Here’s how we do it.

A Life Cycle Built for Government Realities

Our modernization work follows a structured life cycle, honed through years of federal client engagement. Each confronts federal programs’ realities: strict audits, low tolerance for disrupted methods, and constant pressure to achieve more with less.

The life cycle advances through five stages—Legacy Translation and Enhancement; Configuration and Parameter Management; Process Automation; Versioning and Traceability; and Scalable Deployment.

Instead of a full system replacement, our life cycle incrementally evolves legacy systems into scalable, production-ready platforms that preserve the validated analytical logic and institutional knowledge agencies rely on.

Legacy Translation and Enhancement 

The first step in modernization is translating legacy analytical logic into modern languages like Python or SQL. At Summit, we prioritize methodological continuity, safeguarding the validated logic agencies depend on. Once the original logic is replicated, we add targeted enhancements that boost performance, cut technical debt, and unlock new analytical capabilities. This approach preserves institutional knowledge and primes the system for future growth. 

Configuration and Parameter Management 

Many legacy systems rely on spreadsheets to manage parameters, assumptions, and model inputs. Though familiar, these approaches can create risks around version control, transparency, and reproducibility. We replace spreadsheet inputs with structured configuration files and parameter management systems. This enables agencies to update assumptions in a controlled, versioned environment with full traceability of every model run.

Process Automation

Once the underlying logic and configuration structure are modernized, we automate the operational workflow. Tasks once needing manual intervention—running models, validating inputs, generating outputs—now form a consistent, repeatable pipeline. Automation boosts efficiency and reliability. It cuts human error and ensures complex analyses run consistently across teams and reporting cycles.

Versioning and Traceability

For federal programs, transparency and auditability are vital. Our modernization life cycle integrates versioning and execution tracking directly into the platform. Each model run logs inputs, parameters, code version, and outputs, creating a clear audit trail. This traceability ensures internal review, regulatory oversight, and lasting confidence in the analytical results.

Scalable Deployment

The final stage deploys the modernized system into a secure production environment. We deploy solutions on scalable cloud infrastructure—primarily Amazon Web Services (AWS) or Microsoft Azure—using role-based access controls, CI/CD pipelines, and distinct development and production environments. Crucially, we design these platforms for independent operation and maintenance by agencies. Modernization should empower clients with lasting ownership, not foster permanent dependency.

Proven in Practice: DFC and FDIC

At the U.S. International Development Finance Corporation (DFC), we converted federal credit subsidy models from Stata and Microsoft Excel to Python, integrated them into a web-based interface, and deployed a serverless Azure platform supporting multiple model types with full traceability. Result: Faster model runs, fewer execution errors, and a system designed for audit-ready federal finance reporting.

At the Federal Deposit Insurance Corporation (FDIC), we modernized the legacy SAS-based ROAR model—used to estimate receivership asset loss rates—by replacing manual Excel updates with automated data processing, reproducible run metadata, and automated output validation. The FDIC validated the solution, promoted it to production, and expanded the modernization across related receivership models.

The Bottom Line

Legacy systems embody vast accumulated knowledge—years of methodical decisions, regulatory alignment, and stakeholder trust. Modernization done right preserves that history. It boosts durability, transparency, and sustainability, ensuring the analytical systems federal agencies rely on can deliver lasting value.