Sixty yearsof code.Finally translated.
The world runs on yesterday.
Half a century ago, engineers wrote the systems that quietly run the modern world — payrolls, settlements, air-traffic, utility grids. Their authors have retired. Their languages have been declared dead a dozen times. The code persists.
Modernization has remained slow, brittle, and outrageously expensive. We are building the AI that finally makes it tractable.
“The hardest software problem of the next decade isn't building new systems. It's translating the ones we can no longer afford to rewrite by hand.”
An AI that reads, reasons, and rebuilds.
Built from the ground up for the dialects, idioms, and architectural assumptions of 1970–2005. Not a generic copilot retrofit.
Understand intent, not just syntax.
A semantic model of the codebase: control flow, data shape, hidden invariants, and the business intent encoded over forty years of patches.
- Cross-file dependency and call graphs
- Pattern, idiom and anti-pattern detection
- Dead-code, dark-data and ghost-branch surfacing
- Cyclomatic complexity, coupling, drift
Map decades of intent into modern architecture.
Choose your target stack. Our planner reconciles the source intent with idiomatic patterns in the destination — typed, modular, observable.
- Target-aware refactoring strategies
- Domain decomposition and module boundaries
- Schema and storage migration plans
- Risk-ranked execution roadmap
Generate code with provable equivalence.
Production-grade output verified against the source through symbolic execution, generated property tests, and side-by-side replay on real workloads.
- Idiomatic Java, Kotlin, Scala, C#, F#, TypeScript, Python, Go, Rust, C++
- Auto-generated test parity suites
- Differential replay against legacy traffic
- Auditable transformation map per commit
From this. Into this.
Real output. A 1987 COBOL deposit routine, modernized to idiomatic Java with the original business logic preserved line-for-line in the transformation map.
01 * ACCT-DEPOSIT — applies a deposit to a customer balance02 IDENTIFICATION DIVISION.03 PROGRAM-ID. ACCT-DEPOSIT.0405 DATA DIVISION.06 WORKING-STORAGE SECTION.07 01 WS-AMOUNT PIC S9(9)V99.08 01 WS-BALANCE PIC S9(11)V99.09 01 WS-FEE PIC S9(5)V99 VALUE 2.50.1011 PROCEDURE DIVISION.12 PROCESS-DEPOSIT.13 IF WS-AMOUNT > 014 COMPUTE WS-BALANCE = WS-BALANCE + WS-AMOUNT - WS-FEE15 PERFORM LOG-TRANSACTION16 ELSE17 DISPLAY 'INVALID AMOUNT'18 END-IF.
01// Generated by Legacy Modernization AI · semantic equivalence verified02public final class AccountDeposit {03 private static final BigDecimal FEE = new BigDecimal("2.50");0405 public Result deposit(Account account, BigDecimal amount) {06 if (amount.signum() <= 0) {07 return Result.invalid("amount must be positive");08 }0910 var balance = account.balance()11 .add(amount)12 .subtract(FEE);1314 ledger.record(account, amount, FEE);15 return Result.ok(balance);16 }17}
Three movements, one outcome.
- 01/ Sweep
Feed it the codebase. Get the full map.
A complete semantic atlas of your system in hours, not months. Modules, dataflows, dependencies, dead branches, security debt — all surfaced and ranked.
artifact · 01●deep-sweep.report.jsonCyclomatic82Coupling61Dead code34Dark data49 - 02/ Translate
Choose your stack. Watch the transformation.
Pick a target — Java, Python, Go, Rust, TypeScript. The model rebuilds your system idiomatically, with module boundaries that match your domain, not the legacy file structure.
artifact · 02●modernize.plan.diff-PERFORM CALC-INTEREST UNTIL EOF.-MOVE WS-RATE TO WS-OUT-RATE.+interest = computeInterest(account);+audit.record(account, interest);ledger.commit(transactionId); - 03/ Verify
Every transformation comes with proof.
Generated property tests, symbolic equivalence checks, and shadow-replay against production traffic. You get a transformation map, not a black box.
artifact · 03●equivalence.proof.log✓Symbolic equivalence on 1,247 paths✓Shadow replay · 14 days production traffic✓Generated property tests · 312 / 312 passing✓Numeric drift · ±0.000 across all decimals
Sixty years of dialects. One model.
Trained on every major mainframe and enterprise dialect from the punch-card era through the early web. Targeting the modern stacks teams actually want to ship on.
Bring us your hardest system.
We're in the early phase of building what may be the most demanding software product of the decade. Public launch is targeted for Q1 2028. Until then, we're talking to enterprises with mission-critical legacy stacks — to learn what to build first.