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AI 副驾驶辅助的遗留现代化——告别大爆炸重写

通过 AI 辅助重构、回归测试自动生成与治理门禁，逐步告别 COBOL、主机与老化 Java——每个迭代都交付价值。

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The problem

The legacy stack runs the business—and blocks every change

  • 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.

  • 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.

  • 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.

  • 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.

运作方式

Incremental refactoring with AI assistance and governance gates

步骤 1

Mine the knowledge

AI extracts business rules, data flows, and dependencies from the legacy codebase into a queryable knowledge graph your engineers actually use.

步骤 2

Translate and refactor

Governed prompts translate legacy modules into target stacks; regression tests are generated from observed behavior, not from imagined specs.

步骤 3

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.

AI 副驾驶辅助的遗留现代化——告别大爆炸重写

包含内容

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.

技术提供 Enterprise Knowledge AI

成果

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

合作方式

From first call to production—without the usual drag

Assess

Week 1–3

Map legacy stack, business-critical modules, regulatory constraints, and target architecture.

Design

Week 4–6

Define governance gates, target stack, cutover sequence, and how AI assistance fits your security model.

Build

Week 7–14

Mine knowledge, refactor first capabilities, generate regression tests, and ship the first cutover.

Scale

Week 15+

Roll out by capability, decommission legacy modules, expand to adjacent systems.

Timelines vary by legacy complexity, regulatory exposure, and team capacity.

开始

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.

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