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AI reads decades-old code so your modernization plan stops being a guess.

Modernization roadmaps for legacy systems, with AI-extracted requirements and ROI math.

Modernizing legacy systems (COBOL, mainframe, old .NET) is one of the highest-stakes engineering investments. Stride's Legacy Intelligence reads the legacy code, extracts implicit requirements, generates a phased modernization roadmap, and computes payback math grounded in real LOC and complexity.

Why do COBOL and mainframe modernization estimates land 2-5x over budget?

Legacy modernization estimates are usually wrong by 2-5x because nobody actually knows what the legacy code does. The original authors are gone, the documentation is dated, and consultants build estimates from architecture diagrams rather than reading the code. Teams kick off multi-million-dollar rewrites only to discover undocumented business logic that doubles the timeline.

What does Stride extract from legacy code to price a modernization roadmap?

Stride's Legacy Intelligence ingests the legacy codebase, builds a control-flow graph, identifies entry points and external integrations, extracts the implicit business rules (often hundreds per system), and produces a phased modernization roadmap with strangler-fig boundaries and payback math per phase. The output is a defendable plan, not a guess.

  • Static analysis of COBOL, mainframe, .NET, Java codebases: extracts control flow + data flow
  • Business rule extraction with source-code citations (every rule traces back to lines)
  • Modernization roadmap with strangler-fig phase boundaries
  • Per-phase payback math: development cost vs operating cost saved
  • Risk register: identifies the riskiest pieces (high complexity, low test coverage, single owner)
  • Strategic Q&A: "if we replaced the order-processing service, what depends on it?"
Best for

Engineering leaders at companies with significant legacy systems (banks, insurance, government, telecom) planning a multi-year modernization investment.

Not for

Greenfield teams. Teams with codebases under 50K LOC where reading the code yourself is faster than tooling. The AI's value is in the comprehension layer over hundreds of thousands of LOC.

Frequently asked

What languages are supported?
COBOL, RPG, PL/I, mainframe assembler, classic ASP, VB6, .NET Framework, Java (legacy J2EE patterns), Delphi. Modern languages work too but the value is highest on legacy ecosystems where AI literacy is rare.
How accurate is the business rule extraction?
In testing: ~85-92% precision on extracted rules (rule statement matches actual code behaviour), ~75-85% recall (rules the AI surfaces vs all rules a careful human review would find). The remaining gap is for rare conditional branches and undocumented domain knowledge embedded in variable names. Always pair AI extraction with a human review for high-stakes decisions.
Can the AI write the new code?
It can scaffold target-language equivalents and produce migration playbooks, but the editorial position is: AI extracts what the legacy does; humans + AI together design what the new system should be. Legacy code embeds 30 years of business compromises; "rewrite as-is" is rarely the right answer.
How does the payback math work?
For each phase: development cost (FTE-months × loaded rate) vs operating cost reduction (mainframe MIPS savings, license fees avoided, support hours freed). Numbers come from your finance team's inputs + industry benchmarks. The output is a defendable spreadsheet, not a black-box ROI claim.