PE
Principal engineer
Owns the engagement end to end and is in the room from the first call. Not a handover from a salesperson to a delivery team you have never met.
SOC 2 Type II · ISO 27001 · On-prem & air-gapped
Migralith modernizes systems that have outlived the people who built them. Deterministic analysis for ground truth, agents for the volume, and senior engineers for everything a tool cannot infer.
30 minutes with the engineer who would run it. No deck, no discovery questionnaire.
Which one is yours?
Pick the one that keeps you awake and we will say what an Assessment looks at first.
Reads — COBOL · JCL · PL/I · .NET · Java · Kotlin · TypeScript · Terraform
The offering
Three engagements, scoped to the places modernization usually fails. Each one opens with an Assessment that fixes the scope, the risks and the success criteria before anyone touches code.
Logic that spread across clients and services becomes reusable APIs, with one place to change a rule instead of three.
The validated knowledge base your AI tools are missing. Copilot, Cursor, Claude Code and your own MCP servers all query the same source.
.NET, Java, monolith to microservices, mainframe. Taken on directly, with staged delivery and validation at each step.
Why this work is hard
Four people awake because a change to a batch job might touch something in a module written before any of them joined. Nobody can say for certain. The person who could left in 2019.
That is the actual problem — not the language, not the framework. The knowledge that would make the change safe was never written down, and no amount of tooling invents it.
How we do it
Static analysis alone misses intent. Models alone invent. Consultants alone do not scale. All three run on every engagement, in that order.
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.
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.
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.
Engagement model
Four stages, each priced up front, each gated on the evidence from the one before.
A read on the stack and how well it suits modernization, a scoped plan, the risk list and the success criteria. Useful on its own even if you stop here.
The rules, flows and behaviour your engineers and your agents need in order to change the code safely, extracted and written down properly.
The scoped change itself — code, services, APIs or migrated work — delivered in stages you can watch rather than wait for.
Playbooks, training, test suites and a queryable knowledge base, so the system keeps moving after we are gone.
Who you actually work with
You will know who is on your engagement before the contract does. The same people stay on it through handover.
Owns the engagement end to end and is in the room from the first call. Not a handover from a salesperson to a delivery team you have never met.
Drives the deterministic pass, validates the map against your system, and is the person who tells you when the output disagrees with the architecture diagram.
Brought in for the stack you actually have. Reads COBOL copybooks or framework-era .NET the way your longest-serving engineer does.
In their words
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.
Executive Director
Mainframe platform, top-five US bank
From the founder
We kept walking into the same room: a system everyone depended on, and nobody left who understood it. That is not a staffing problem.
Migralith started because the tools we were handed kept answering confidently and wrongly on exactly the code that mattered most. So we built the analysis first, and only then let the models near it. Every engagement since has been shaped by the engineers who have to live with the result after we leave.
Questions
Start with one codebase
Thirty minutes with the people who would actually run the work. We will tell you which step produces evidence soonest — or that you do not need us.
Book a scoping call