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Capability

AI-Accelerated Modernization

The unglamorous work AI is genuinely good at: reading a codebase nobody remembers writing, documenting it, testing it, and migrating it without a big-bang cutover.

What is actually included

  • Codebase archaeology and documentation for systems that lost their authors
  • Test coverage built retroactively so a refactor is safe to attempt
  • Framework, language, and platform migrations staged for low risk
  • Dependency, security, and end-of-life remediation at scale

The system nobody wants to touch

Every organisation of any age has one. It works. It is important. The people who wrote it have gone, the documentation was never written, and the test suite is either absent or has been failing so long that nobody looks. Any proposal to change it gets priced defensively, because the honest answer to “what will this break” is that nobody knows.

This is the work AI is genuinely, unglamorously good at. Reading a large unfamiliar codebase and explaining what it does is exactly the shape of task these tools handle well, and it is the task that used to consume the first two months of any modernization project.

The sequence

  1. Read and document the system as it actually is, including behaviour nobody intended but something now depends on.
  2. Build characterisation tests against current behaviour, so a refactor has something to be measured against.
  3. Identify what can change safely, what needs a strangler approach, and what should be left alone.
  4. Stage the migration so each step is independently deployable and independently reversible.
  5. Leave the documentation and the tests behind as the durable output.

Step two is the one people try to skip, and it decides whether the project succeeds. Tests written against the old system are what let you prove the new one behaves the same way. Without them every cutover is a leap of faith, and gets treated as one by whoever has to approve it.

Also in scope

  • End-of-life runtime and framework upgrades, staged rather than big-bang
  • Dependency and vulnerability remediation across a large surface
  • Extracting a service out of a monolith without a rewrite
  • Rebuilding a system whose original vendor has disappeared

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