Macros, PROCs, copybooks, and how files call each other. Suggestions use that context, so a fix in one program does not break the next.
Hard sources — not covered by free tools
Also parsed — certify what free tools miss
Runtimes
After migration
MigryX AI
Deterministic parsers rewrite SAS, mainframe batch, and legacy ETL. MigryX AI takes the blocks they cannot resolve, and every change it makes is checked row by row before anyone accepts it.
One loop
Nothing is accepted until the outputs match the original. Databricks is the first target for SAS and mainframe batch.
Source-specific parsers rewrite the code deterministically, to Databricks first.
Data Matching compares the target output to the original, row by row.
MigryX AI reads the exception report and proposes a fix for the blocks that differ.
The fix is validated again. It is accepted when it matches, or it goes to an engineer.
It sits inside the conversion. It does not replace the parser, and it does not paste your code into a chat window.
Macros, PROCs, copybooks, and how files call each other. Suggestions use that context, so a fix in one program does not break the next.
When a block fails parity, MigryX AI reads the mismatch and the logs and proposes a change. The parser output stays the baseline.
Point MigryX AI at a hosted model or at a private model in your own environment. Air-gapped estates can also run with AI turned off. The parsers still convert and validate.
Models
MigryX AI calls the endpoint you choose. A private model inside the enterprise is fine. The parser output is still checked row by row, whichever model proposed the change.
Databricks first
PySpark, Delta, and Workflows, with the same row-level check whether a block came from the parser or from MigryX AI.
Auditors get the exception report and the parity result, not a claim that an AI was confident. If a row does not match, it is not accepted.
Send a program and get a coverage report plus a converted sample. Or bring a workload to a 30-minute demo.