Accelerating data modernization and tech debt reduction with AI-enabled delivery

This privately held investment management firm specializes in a comprehensive suite of investment products, risk management solutions, and advisory services. Managing funds for investors and institutions worldwide, the company operates globally and depends on highly reliable, well-governed data systems to support critical financial decisions.

The organization faced significant technical debt across legacy data platforms while undertaking large-scale cloud and analytics migrations.

Core business logic was embedded in:

This created high migration risk and deep dependency on a small group of subject-matter experts. Manual coding, documentation, and validation processes slowed upgrades and increased regression risk.

Rather than treat modernization as a one-time migration effort, the team restructured delivery itself.

GenAI and agentic workflows were embedded directly into development and migration processes, not just to generate code, but to:

By integrating AI into familiar developer environments and CI/CD pipelines, the team accelerated delivery without disrupting client tooling mandates or compliance standards.

Modernization shifted from a manual rewrite project to a scalable, AI-driven way of working.

GenAI was applied to systematically reduce technical debt and modernize embedded logic across data platforms.

Legacy SQL procedures and Kedro workflows were translated into:

In one case, over 1,400 lines of complex SQL were simplified into concise, structured outputs that dramatically improved readability, maintainability, and review efficiency.

A custom Python-based command-line tool leveraging the GitHub Copilot API generated standardized templates (HTML, markdown, YAML, Python) and automated source-to-target mappings. Artifacts were auto-published to Confluence, ensuring documentation remained synchronized with code.

Agentic workflows orchestrated across PyCharm, Python, Jinja templating, and Copilot enabled efficient migrations to AWS and Databricks while optimizing cost, performance, and developer productivity.

Most importantly, validation and standards were embedded directly into delivery workflows — reducing regression risk and increasing confidence in production data.

Modernization evolved from a slow, manual effort into a scalable delivery model — enabling faster releases, fewer regressions, and greater organizational confidence in data systems.