Migralith //

A modernization service, not a tool. Four gated stages, fixed price each.

How an engagement runs

How it works

Deterministic first.
Then agents. Then people.

In that order, on every engagement. Each layer covers the failure mode of the one next to it — that is the whole design.

The stack

Three layers, working together

Ground truth

We read the code before anyone touches it.

Our own engine walks every repository and records what is actually there — dependencies, entry points, data flows, dead code, and the relationships that cross repository boundaries. Run it twice on the same commit and the map comes back the same, which is what makes it checkable rather than impressive.

  • 01Dependency and call-path mapping across repositories
  • 02Entry points and data flows traced end to end
  • 03Dead code and duplication identified, not estimated
  • 04Validated with your engineers before anything is proposed
Volume

Models do the repetition, anchored to something that cannot drift.

Agents apply patterns that have already been verified, across however much code the engagement covers. Because they work from the analysis rather than from a sample of the source, the thing that normally breaks at scale — a confident guess about code the model never read — has nowhere to start.

  • 01Verified patterns applied repository-wide
  • 02Every action traceable to the analysis behind it
  • 03One shared system context, not a guess per session
  • 04Progress visible in your workspace as it happens
Judgement

Nothing ships on a best-effort basis.

Engineers who have done this before review what comes out, add the knowledge that was never written down anywhere, and catch the cases no tool can infer. This is the step automated modernization skips, and it is why the output survives contact with the people who own the system.

  • 01Human review on every deliverable
  • 02Undocumented rules captured from the people who hold them
  • 03Edge cases automated analysis cannot reach
  • 04Validation evidence attached to each stage
An engineer reading through source on a second monitor
The step tools skipSomebody still has to read it
and say what it means.

The engine

What the analysis
actually produces.

The engine is deliberately boring: it reads the code and records what is there. Dependencies, entry points, data flows, dead code, duplication, and the relationships that cross repository boundaries where most tools stop looking.

The point is that you can check it. Run it twice on the same commit and the map comes back the same, which is what makes it usable as ground truth for everything downstream — the agents, the specification and the migration itself.

  • Legacy: COBOL, JCL, PL/I
  • Managed: .NET, Java, Kotlin
  • Web: TypeScript, JavaScript
  • Cross-repository by default, queryable over MCP

Security posture

For codebases that cannot leave the building

Both certifications held, and a deployment model for whatever your security review requires.

01

SOC 2 and ISO 27001

Both certifications held, with the audit trail to match.

02

On-premise and air-gapped

Deployable inside your perimeter, including environments with no outbound network.

03

Customer-managed models

Point the engagement at your own LLM endpoints. Your code and your inference stay yours.

04

Proven at volume

More than a hundred million lines have been through the analysis engine.

05

Mainframe and legacy

COBOL, JCL and PL/I alongside the modern stack, in one map.

06

Stays current

The understanding is refreshed as the codebase moves, not frozen at hand-off.

In their words

What the engineers said afterwards

They handed us a map of the application and our own analysts checked it line by line. It held up. Every detail the business side had been asking for was in there.
ED

Executive Director
Mainframe platform, top-five US bank

0M+Lines of code put through the analysis engine
0Gated stages, each priced up front and each useful alone
0Certifications held: SOC 2 Type II and ISO 27001

Questions

About the technology

It is static analysis, not inference. The engine parses and resolves rather than predicts, so the same input produces the same map every time. That property is what lets the agent layer sit on top without compounding its own errors.
They handle volume. Patterns are verified once against the analysis, then applied across however much code is in scope, with every action traceable back to the analysis that justified it.
The knowledge that was never written down, and the judgement calls no tool can make. They also review everything before hand-off, which is what keeps the output credible with the engineers who own the system.
It is refreshed as the codebase moves rather than frozen at hand-off, and Enablement gives your team the playbooks to keep it current once the engagement closes.
Yes. On-premise deployment is supported, air-gapped environments included, and you can use your own managed LLM endpoints so inference stays inside your boundary too.

Start with one codebase

See what the engine finds in your codebase

The Assessment is scoped to the smallest piece of the estate that will produce real evidence.

Book a scoping call