MADIS · Legacy System Modernization with AI
You can't transform a system you don't understand.
Legacy systems run your business — but nobody fully understands them anymore. Original developers are gone, documentation was never written, and decades of business logic are buried in code no one dares to touch. Every modernization attempt that skips a structured understanding phase fails. Not eventually. Inevitably.
- 60–80%
- of enterprises carry critical legacy debt
- 75%
- of legacy modernization projects fail before delivering value
- 0
- inference — dependencies and rules are extracted with compiler fidelity
Why modernization fails
Neither the old playbook nor the new one works on its own. Conventional migration strategies and naive AI shortcuts fail for related reasons: both skip the step where the system's real structure gets understood before anyone starts changing it.
Conventional approaches
- Lift-and-shift — moves problems to a new environment; doesn't solve them.
- Big-bang rewrites — run years over schedule and are frequently abandoned.
- Microservices too early — creates distributed monoliths, not simplicity.
Naive AI approaches
- AI-fed code dumps — hit context limits, hallucinations, structural blindness.
- Context window limits — a legacy codebase simply doesn't fit.
- Hallucination — plausible but wrong answers carry hidden business risk.
- Structural blindness — LLMs can't build call graphs or dependency trees on their own.
- Lossy compression — hierarchical summarization silently discards critical facts.
“LLMs should narrate facts — not discover them.”
The 3-phase platform
Understand first. Then plan. Then execute. Each phase produces something concrete you can act on — a knowledge graph, a backlog, a shippable change — before the next one begins.
Assess & Understand
Roslyn and Oracle metadata APIs extract every dependency, data flow, and business rule with compiler fidelity — no inference. AI narrates the findings as documentation.
Plan the Transformation
The assessment becomes a prioritized backlog. Business stories come first, enriched with technical constraints — no premature architecture decisions.
Execute with AI Agents
AI agents implement changes via implementation-ready prompts, with configurable oversight — from full review to autonomous delivery.
Where the MADIS approach differs
Built for accuracy and control, not just speed.
Compiler-grade fidelity
Every dependency and rule is resolved with the same accuracy as the compiler — not estimated.
On-premises & private
Runs inside your infrastructure. Source code, schemas, and business logic never leave your perimeter.
No vendor lock-in
A standards-based knowledge graph (Neo4j + pgvector). No proprietary runtime — exit anytime.
Extensible by design
Java, TypeScript, Python, COBOL — pluggable extractors for whatever your stack actually is.
Ready to understand your legacy system?
Start with Phase 1 — a standalone assessment that gives you a complete, compiler-grade knowledge graph of what you're actually running.
Looking for something else?
lsm@madis.hu · Solution: Legacy System Modernization with AI · Provider: MADIS Consulting