YOUR PROBLEM
Nobody knows how it works. Including the people who maintain it.
The system has been running for 15 years. The original developers are gone. The documentation—if it exists—was last updated in 2017. There are business rules scattered across COBOL, stored procedures, and configuration tables.
You need to modernize it. But first, you need to understand it.
THE LEGACY TRAP
"Discovery phase"
Interview stakeholders. Read old documentation. Trace through code. Build a partial picture.
"Build the new system"
Based on discovered requirements. Realize discovery was incomplete. Go back for more discovery.
"Testing phase"
Run parallel systems. Compare outputs. Find discrepancies. Discover more undocumented behaviors. Update new system.
"The reckoning"
Deploy. Find edge cases in production. Emergency fixes. Stakeholder trust erodes. Consider rolling back.
Total cost: $2-5M. Timeline: 18-24 months. Success rate: ~40%.
THE INSIGHT
What if you could extract the specification from the code?
The knowledge is in the code. It's just buried under decades of implementation details. What if you could extract the business logic as an executable specification?
Not documentation. Not a summary. An executable specification that's mathematically equivalent to the original system—but readable, maintainable, and portable.
SEE IT IN ACTION
Watch a 47,000-line COBOL system get analyzed—including undocumented behaviors.
WHAT THIS MEANS FOR YOU
Understand legacy systems
Extract the actual behavior—including undocumented edge cases—as executable specifications.
Prove migration equivalence
Mathematical proof that new system behaves identically to old. No "hope and test."
Incremental modernization
Extract specs from legacy. Run them on modern infrastructure. Migrate piece by piece with confidence.
Knowledge preservation
Tribal knowledge becomes formal specifications. Survives retirements and reorganizations.
AND THIS IS JUST ONE APPLICATION
The same technology that does this also...
Accelerates releases
Impact analysis in seconds. Ship weekly instead of quarterly.
Makes AI controllable
AI writes specs, machines verify. Auditable by design.
Automates compliance
Audit trails generate themselves. Every decision explainable.
Replaces tests with proofs
Mathematical certainty. Not statistical confidence.