Use Cases
One platform, many paths. All of them yours.
Every transformation path runs on the same engine. Start with the one that solves your most pressing problem — the others stay open.
The issue
Mainframe costs and risk keep climbing — but a rewrite is the one thing you can't afford.
The SHIFT approach
→Scan the workload and fix the scope
→Compile the original code natively for Linux and cloud
→Cut over incrementally — integrity preserved
Off the mainframe fast, with data and source integrity preserved.
Mainframe
costly, rigid
SHIFT
⟶
compiles natively
Linux / Cloud
incremental
The issue
Decades of accumulated code — nobody knows what's still alive.
The SHIFT approach
→Run legacy and modern languages in one environment
→Dead code surfaces naturally as you work
→Refactor non-disruptively, at your pace
A cleaner estate, refactored progressively while the existing workload continues to operate.
Legacy code
COBOL, PL/I
SHIFT
⟶
one environment
Clean estate
dead code gone
The issue
Your target architecture is blocked because legacy data can't be touched.
The SHIFT approach
→Modern code reaches legacy datastores directly
→Design and validate the new architecture in parallel
→Switch when proven — not before
A validated architecture, with the existing workload continuing to operate along the way.
Modern code
Java, Python, Go
SHIFT
⟶
direct access
Legacy datastores
untouched
The issue
Some parts genuinely deserve a rebuild — but nowhere near all of them.
The SHIFT approach
→The SDK opens legacy and modern datastores to every language
→Rebuild the value-adding parts with true backward compatibility
→Everything else keeps running as-is
Reimagined where it pays off; untouched where it doesn't.
Your estate
legacy + modern
SHIFT
⟶
backward compatible
Rebuilt
only what adds value
The issue
Auditors and regulators want decades of records — in their original form.
The SHIFT approach
→Move data and code to an open platform with data integrity preserved
→Original-form retention with a complete audit trail
→Modern reporting, KYC, and regulatory access with minimal effort
Compliance-grade retention, reachable by modern tools.
Original records
decades, as-is
SHIFT
⟶
data integrity preserved
Open platform
reporting · KYC
The issue
One vendor controls your runtime, your pricing, and your roadmap.
The SHIFT approach
→The same code and data run interchangeably on mainframe and Linux
→Choose active-active, active-passive, or dual-stack
→Negotiate from strength — or leave
Lock-in broken, compatibility intact.
One codebase
unchanged
SHIFT
⟶
same code & data
Two stacks
mainframe + Linux
The issue
Test environments on the mainframe are expensive and scarce.
The SHIFT approach
→Spin up production-compatible environments off the mainframe
→Test with greater confidence against production behavior
→Pay a fraction of mainframe test/dev cost
Greater testing confidence at a fraction of the cost.
Production
on mainframe
SHIFT
⟶
compatible environment
Test env
fraction of the cost
The issue
The applications, data and business logic AI needs are often trapped inside legacy environments that modern tools cannot easily reach.
The SHIFT approach
→Move the existing workload into the SHIFT environment
→Preserve the application, business logic and data
→Expose selected functionality and data through modern interfaces
→Connect modern applications and AI without rewriting the legacy system first
Make decades of enterprise knowledge accessible to modern applications and AI.
Legacy workload in SHIFT
applications · logic · data
SHIFT
⟶
controlled access
AI / Modern applications / APIs
via interfaces
The issue
AI can help modernize software — but legacy applications are often isolated from the modern development environments, tooling and context AI needs.
The SHIFT approach
→Run the existing workload in a modern development environment
→Make source, data, APIs, dependencies and tests available as development context
→Use AI tools to help understand, document, test and refactor applications
→Modernize individual components progressively while the existing workload continues to operate
Bring AI into the modernization process without turning modernization into another big-bang rewrite.
AI tools
Legacy workload
continues to run
SHIFT
Environment
- Source
- Data
- APIs
- Dependencies
- Tests
Development environment
Modernized components
progressively
Not sure which path fits?
Tell us what you need to achieve. We'll find the cleanest path there — hard problems, made simple.