2: A phased approach separates what’s expensive from what’s fast

Analyzing a complex Java codebase is a time-consuming and expensive process. Done properly, for one sport and one module, it takes 30–60 minutes of AI processing, with human checkpoints along the way.

But turning structured data into a readable specification document is fast (and therefore relatively cheap). Given the right input containing extracted context, AI can generate a comprehensive spec in five to 15 minutes. And if the format needs changing or a section needs restructuring, it’s easy to just rerun the generation step, with no need to re-analyze the codebase. 

This makes it possible to extract business logic from multiple sports in parallel, saving even more time. A new sport can go from zero to implementation-ready specs with around 30 to 45 minutes of setup, four to six hours of parallel extraction and six to eight hours of SME review.

3: A shared context layer keeps AI informed

Lost context is one of the biggest barriers to AI acceleration. Every session starts from scratch, and without any context, AI improvises, leading to inconsistent, unusable results.

The shared steering files played a big role in providing context for every AI session. We also maintained architecture decision records (ADR)  that captured every significant structural choice, so the AI followed decisions the team had already made, rather than guessing.

This shared context layer also meant that the benefits of every improvement decision were inherited by each future sport, amplifying investments in quality.

4: Code is generated directly from specs

The purpose of the business logic extraction was to close a loop that most migrations don’t: The legacy codebase goes in, structured specifications come out, and the new platform is built from the specs, rather than guesswork or memory.

Our approach meant that the specs were structured enough to feed directly into code generation. The team building the new platform could build from a reviewed description of the behavior encoded in the legacy system.

Specs from code were also more likely to surface behavior nobody had written down. The behavior was translated into a structured spec that could be reviewed and deliberately carried forward or changed.

Accelerating migration without compromising accuracy

The shift to an AI-assisted reusable framework massively reduced the company's migration timelines. The framework turned undocumented tribal knowledge into structured, reviewable assets that directed SMEs' attention to where it was actually needed.

By applying AI within this framework, a 10-sport migration program that would have taken two to three years was reduced to around three to four weeks of total effort — or as little as one to two days with parallel execution. Onboarding time for a new sport dropped from 10 to 15 weeks to less than a day, because each sport inherits shared templates.