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Mainframe & COBOL modernization

COBOL is load-bearing.
We treat it that way.

The COBOL on your mainframe is not a relic. It runs the ledger, the policy engine, the overnight batch the whole company depends on. We modernize it one slice at a time — COBOL, mainframe DB2, and JCL read together, parity proven against the mainframe before any cutover.

Book a discovery call How the slice loop works →
The stack

Decades of business reality, written in COBOL.

Estimates of how much COBOL still runs in production range from roughly 220 billion lines (Reuters, 2015, via IEEE Spectrum) to upper-bound figures near 800 billion (Micro Focus) — present it as a range, because nobody has counted it precisely, and the honest answer is that there is a lot of it. It carries core banking, insurance, payroll, and government systems that have been correct for forty years. The code is not the problem. The problem is the people. The COBOL workforce is aging — the average mainframe programmer is around 58, and on the order of 10% retire each year (IBM, via Fujitsu) — and the graduates entering the field are not lining up to learn a stack they see as having no future. The risk is not that the COBOL stops working. It is that the people who understand it leave faster than they can be replaced.

What modern looks like

A COBOL estate, re-expressed for the next decade.

For a COBOL/CICS/DB2/JCL estate the target is concrete: business logic re-expressed as Java or C#/.NET services, VSAM and DB2 data moved onto a modern data platform, and the batch and CICS transaction flows surfaced as clean APIs. Which language, which cloud, which data platform — that is chosen with you in Discovery, not dictated by the source, and it can land in a regulated or air-gapped environment. What we hold constant is the shape of a system built to last.

Domain services, not paragraphs
The business rules buried across thousands of COBOL paragraphs are surfaced as explicit, testable domain services — the logic finally readable by people who never learned COBOL.
Event-driven data, off DB2
Mainframe DB2 tables and the overnight batch they feed are re-expressed as optimized, event-driven data stores. Batch windows stop dictating the calendar.
A cloud-portable API layer
A modern API layer replaces direct CICS and program-to-program calls, so new channels can be built without touching the mainframe.
A talent-friendly stack
The result runs on a modern language and platform your existing engineers — and the ones you can actually hire — already know.
How we do it

The slice loop, applied to the mainframe.

A slice is a single behavior — one batch job, one online transaction. Each is analyzed, rebuilt, validated in shadow against the mainframe, and promoted only on parity. A strangler facade shifts traffic gradually; the COBOL retires last.

01
Analyze
AI deep-reads the COBOL, the copybooks, the JCL, and the DB2 schema together — programs, paragraphs, batch dependencies, and data flows captured as a living spec. The analysis runs 10× faster than manual review: a typical pass goes from 12 weeks to 2.
02
Transform
One behavior at a time — a single batch job, a single online transaction — is rebuilt on a modern target. Scope stays small enough that the team and the AI can reason about it end to end.
03
Validate
The new slice runs against the mainframe under shadow traffic until output, data integrity, and the corner cases match. The parity gate stands between every slice and production.
04
Cut over
A facade in front of the mainframe shifts traffic gradually. Diverge, and it rolls back. The COBOL program is decommissioned only once the modern slice is proven.
Where the advice stops
Slice-by-slice depends on being able to isolate a behavior and observe it in production. Some mainframe estates resist that: batch windows so tightly coupled that no single job can be carved off, undocumented JCL and COBOL interdependencies that only surface once you pull the thread, real-time CICS paths that are hard to shadow cleanly without touching live traffic. Where that is the case, the first slices take more groundwork — and we say so in Discovery, not once the work is already underway. Naming the hard cases up front is part of the method. Read the full approach →