AI Modernization
Answers failure mode 3 — the data is trapped in legacy code.
Move legacy code forward, without the rewrite gamble.
Read decades-old systems, understand them, and start with an analysis deliverable you own — before a single line is converted or a budget committed.
Often the reason the data is fragmented (failure mode 3) is that it's locked inside systems written decades ago, in languages no one wants to touch. Modernization reads those systems and frees what's inside — analysis first, conversion second.
The legacy problem
Rewriting from scratch is where migrations die.
The understanding step gets skipped — and then regretted.
A legacy system holds an organization's most sensitive business logic, much of it undocumented. Teams either freeze it (and the data stays trapped) or rewrite it blind (and the project fails). The way through is to read the old code first — map what each part does, score complexity and risk — and only then convert, with the analysis as a deliverable on its own.
How it works
Parse, score, convert, analysis before conversion.
Legacy stays. The black box doesn't.
What you get
Capabilities, not a feature list.
Legacy parsing
Reads the languages others avoid.
Risk scoring
Complexity and risk, quantified.
Rules-driven conversion
Behavior preserved, not guessed.
Modern targets
A stack your team can own.
Analysis as a deliverable
Valuable before a line is converted.
The strong part, named
The analysis is real, the full pipeline is staged.
The parsing, structure-mapping, and complexity/risk analysis are tested and real — and valuable on their own, before a single line is converted. The end-to-end conversion pipeline and the operator front end are in progress. Start with the analysis; decide from there.
Live today
- Legacy parsing
- Complexity + risk analysis
- Analysis report deliverable
In progress
- End-to-end conversion pipeline
- Operator front end
On the roadmap
- Self-serve conversion console