Legacy Modernization with AI
Modernizing legacy systems — recovering knowledge, tests, step-by-step migration with AI.
01 · The problem
The legacy system works and makes money — and slows everything else down. There is no documentation, the authors have left, and every change is a risk.
AI is good at reading large unfamiliar codebases, explaining them and helping cover them with tests. But “rewrite everything with AI” leads to the same system nobody understands — only newer. You need an engineering strategy.
02 · When you need it
- Changes to the system have become too slow and risky
- Knowledge of the system rests with one or two people or has been lost
- An outdated stack gets in the way of hiring, security and growth
- A “rewrite from scratch” has already been tried and failed
03 · What we analyze
- The system's architecture and boundaries — what can be separated
- Business-critical scenarios and their test coverage
- Data and integrations — the hardest part of any migration
- Risks — what will break first and what it will cost
- Readiness of the team and the delivery pipeline
04 · How we work
-
step 01
Knowledge recovery
With AI we build a description of the system — structure, scenarios, dependencies — and verify it with the people who know it.
-
step 02
Safety net
Characterization tests capture the current behavior — before any changes.
-
step 03
Strategy
We choose the approach — gradual replacement, module extraction, stack migration — and the sequence of steps.
-
step 04
Step-by-step migration
Small, verifiable steps. AI speeds up routine work; tests and reviews keep the result under control.
05 · What you get
The exact set of deliverables is agreed after the diagnostic. I don't promise effect figures in advance — they depend on the starting point and are written into the plan as criteria.
- 01A description of the system — architecture, scenarios, dependencies
- 02A set of characterization tests for critical scenarios
- 03A modernization strategy with stages and risks
- 04An AI-assisted migration process with quality control
Related services
Next step
Let's discuss your project
Tell me what is happening with your development and what you expect from AI. I reply personally and suggest a format — a diagnostic, an audit or a consultation.