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Legacy Science Code Modernization Studio

A specialized boutique service that pairs domain-expert scientists (hydrogeologists, geochemists) with senior full-stack modernization engineers to incrementally refactor legacy FORTRAN/C scientific software into modular, testable, cloud-ready Python/Julia codebases while preserving exact numerical outputs and validation against historical datasets. The team works embedded with the agency for 6–12 month engagements, delivering staged releases that run in parallel with legacy systems until cutover is proven safe.

SERVICE

50 weeks • 70% confidence

Value Proposition

Eliminates the false choice between 'keep broken legacy code' and 'rebuild from scratch and lose 20 years of calibration.' Agencies get a working modern codebase, full audit trail of numerical equivalence, and retained institutional knowledge—no offshore outsourcing, no black-box rewrites, no loss of data fidelity.

Target Audience

USGS, EPA, NOAA, DOE national labs, and state geological surveys with mission-critical legacy scientific software (MT3D, FEFLOW variants, groundwater/climate models) and $500K–$2M annual modernization budgets.

Key Features

Parallel validation framework: new code runs alongside legacy, outputs compared to <0.1% tolerance before cutover
Embedded domain expert (hydrogeologist/geochemist) paired with modernization lead for 6–12 months
Staged refactoring: UI layer → numerical engine → data I/O, each stage independently testable and deployable
And more, with full implementation detail...

Tech Stack

Python 3.10+, NumPy, SciPy, Numba (numerical computation) Julia (optional, for high-performance solvers) HDF5/NetCDF libraries (data I/O modernization) FastAPI or Flask (REST API layer)

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Original Problem

Government agencies struggle to modernize legacy scientific software systems while maintaining compliance and functionality

Federal agencies like the Department of the Interior are stuck with outdated scientific software (MT3D-USGS) that's critical for environmental and geological research but increasingly difficult to maintain, update, and integrate with modern systems. Current solutions fail because they require specialized expertise in both legacy code and modern development practices, while agencies face budget constraints, procurement delays, and the need to preserve decades of scientific data and functionality during modernization.

Score: 47.5% • 2 demand signals

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