Mine the knowledge
AI extracts business rules, data flows, and dependencies from the legacy codebase into a queryable knowledge graph your engineers actually use.
/ IT & Daten /
Legacy-Modernisierung mit KI-Copiloten, nicht mit Big-Bang-Rewrite
Raus aus COBOL, Mainframe und alterndem Java mit KI-gestütztem Refactoring, generierten Regressionstests und Governance-Gates—Wert in jedem Sprint.
The problem
The legacy stack runs the business—and blocks every change
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COBOL and mainframe bottleneck delivery
Critical logic lives in code nobody under fifty understands. Every change becomes a multi-team coordination exercise, and new features queue behind the legacy team.
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Knowledge retires with people
Documentation is patchy at best. As original authors leave, the safe answer to 'why this rule exists' is 'don't touch it'—and the modernization plan stalls.
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Big-bang rewrites fail
Two-year rewrites lose sponsorship, miss the business reality that moves under them, and end as parallel systems neither team wants to own.
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No audit trail on the current code
When auditors ask what the system actually does today, the answer is a person plus a debugger. Modernization without that lineage is a leap of faith.
So funktioniert es
Incremental refactoring with AI assistance and governance gates
Translate and refactor
Governed prompts translate legacy modules into target stacks; regression tests are generated from observed behavior, not from imagined specs.
Ship and govern
Incremental cutover by capability with rollback, audit trail of every AI-generated change, and CISO-ready evidence of human review.
Flow adapts to your target stack, regulatory context, and team velocity.
Legacy-Modernisierung mit KI-Copiloten, nicht mit Big-Bang-Rewrite
Leistungsumfang
Everything you needto modernize without the freeze
Code mining, AI-assisted translation, governance gates, and audit trail in a single layer—delivered with your engineering team owning the rollout cadence.
Code knowledge mining
Business rules, data flows, and dependencies extracted into a queryable graph engineers can search and trust.
AI-assisted translation
Governed prompts translate modules into the target stack, with human review gates and policy enforcement.
Incremental refactoring
Capability-by-capability cutover with feature flags, rollback, and parallel-run validation.
Regression test generation
Tests synthesized from observed legacy behavior—not from incomplete specs—so cutover risk is measurable.
Governance gates
Security, IP, and compliance review built into the AI workflow; no ungoverned co-pilot sprawl.
Documentation generation
Up-to-date docs are a byproduct of the migration, not a side project that ages out of date.
Unterstützt von Enterprise Knowledge AI
Ergebnisse
What changes when this runs in production
Results vary by legacy stack, regulatory context, and team maturity. We scope honestly before we promise precisely.
40–60%
less time per module to refactor vs. manual baseline
Orientative—varies by language and code quality.
12 weeks
median time to first production capability cutover
Orientative—based on early implementations.
Full
audit trail of AI-generated and human-reviewed changes
So arbeiten wir
From first call to production—without the usual drag
Assess
Assess
Week 1–3
Map legacy stack, business-critical modules, regulatory constraints, and target architecture.
Design
Design
Week 4–6
Define governance gates, target stack, cutover sequence, and how AI assistance fits your security model.
Build
Build
Week 7–14
Mine knowledge, refactor first capabilities, generate regression tests, and ship the first cutover.
Scale
Scale
Week 15+
Roll out by capability, decommission legacy modules, expand to adjacent systems.
Timelines vary by legacy complexity, regulatory exposure, and team capacity.
Mehr erfahren
Modernization, AI co-pilots, and what to explore next
Loslegen
Ready to ship modernization instead of waiting two years?
No commitment. We start with a scoped session to map your stack, constraints, and target architecture.