Migration readiness
If you are planning to use AI to help move off the mainframe, this is the input that decides whether it works.
AI-assisted code translation is real and it is improving quickly. It also fails in a specific, predictable way, and that failure is avoidable.
The problem
A language model reading raw COBOL is working with a keyhole view. It cannot see the whole program at once, so it infers. It infers which fields share the same bytes under a REDEFINES. It infers what a copybook becomes after substitution. It infers whether a picture clause means a fixed-point decimal with two implied places or something a floating point type will handle differently at the millionth transaction. It infers the flow through a PERFORM THRU range.
Most of those inferences are right. The ones that are wrong do not announce themselves. They surface in testing if you are fortunate, and in production if you are not.
There is a second failure that is less discussed. Large programs exceed what a model can hold at once, so they get split, and each piece gets designed independently. The result is technically working code with no coherent structure, which is a maintenance problem you have simply moved rather than solved.
What we provide
A migration readiness package turns the inferences into stated facts, and gives the work a structure before any code is generated.
- Resolved data definitions: record layouts fully expanded, overlapping definitions made explicit, and numeric fields described with their actual precision and scale so they map to the correct target type rather than a plausible one
- Real control flow: the flow graph of each program, including the paths that fall through, so unstructured logic can be restructured deterministically instead of interpreted
- Unit boundaries and interfaces: a defensible decomposition of the estate into pieces that can be migrated independently, with the interfaces between them stated rather than discovered
- Definition and use chains: what each piece of the system reads and writes, so nothing is quietly dropped
- The environment: CICS transaction state, JCL orchestration, dataset handling and database access paths, which are usually most of the real work and are invisible to code-only tooling
- Traceability: every element referenced back to its source, so the output of the migration can be audited rather than trusted
How it is used
The package is designed to be consumed by whoever is doing the migration, including your own team, your systems integrator, or an AI transformation platform. We are not competing with those. We are supplying the thing they all need and none of them produce.
Verification runs both directions
Because the generated system can be modeled the same way as the original, the two are comparable: call structure, data flow, external touchpoints. That turns "the AI says it is equivalent" into evidence.
What it does not do
It does not make migration automatic, and we would be careful of anyone who tells you otherwise. Character encoding differences that change sort order, arithmetic overflow behavior, storage that is uninitialized but happens to work, abend and syncpoint semantics. These are real and they need differential testing against production data. What readiness does is remove the guesswork from the part that can be made certain, and make the rest visible early instead of late.
Planning a migration?
If you are evaluating AI-assisted modernization, we can tell you quite quickly whether your estate is a good candidate and what would need resolving first.