mLogica Launches AI-Powered + Deterministic Modernization Platform for Mission-Critical Mainframe Transformation
mLogica LLC launched its AI-Powered + Deterministic
Modernization platform, directly addressing what Gartner identifies as the core
failure mode in 2026 mainframe exit programs: the overestimation of generic
generative AI. Where generic AI produces non-deterministic output that cannot
be certified before cutover, mLogica’s LIBER*M platform applies purpose-built
Small Language Models (SLMs) trained on real enterprise production code, COBOL,
Assembler, Assembler TPF, PL/I, Easytrieve, Telon, JCL, SAS, Rexx, IMS, Db2,
and more, through deterministic pipelines that produce identical, auditable
results on every run.
“Mainframe exit programs have failed at rates above 60
percent for 25 years. Rewrite or Reimagine has previously failed 90 percent of
time. The pattern is always the same: the promised solution has no knowledge of
your production estate,” said Amit Okhandiar, President and CEO of mLogica.
“Built on our expert systems based highly automated rules engines with decades
on training data, our SLMs are trained on actual enterprise production code,
not internet descriptions of it. That is the difference between a model that
can certify its output and one that cannot.”
Governed AI at Every Stage
LIBER*M delivers three governed stages: Understand (AI assisted
automated estate discovery and business logic recovery before a line of code is
touched), Transform (SLM-driven code conversion and data migration through
deterministic, slice-based pipelines), and Operate & Evolve (AI-assisted
LLM-driven code enhancements, CI/CD, test generation, and environment
automation within approved guardrails post-cutover).
At every stage, mLogica’s AutoTest Suite augmented by
AutoManager for post cutover run-operate-maintain phase, produces a
release-gate evidence package, what changed, how it was validated, and why the
system is certified safe to promote. In regulated industries, that package is
the condition of production readiness.































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