step 01
Read the whole thing first
deepseek-v4-flash Map modules, entry points, data access and the business rules buried in them, before writing a line. With up to 1M tokens of context a module goes in whole, along with the headers it depends on.
use cases · Code migration
Move the code nobody wants to touch
The system that runs the business, written in something you cannot hire for, with the person who understood it retired. Migration is a reading problem before it is a writing problem, and reading a million lines is exactly what a long context window and a flat rate are for.
how it works
How it is built.
Three steps, with the component that does the work in each one.
step 02
Translate against tests, not vibes
deepseek-v4-flash Generate the characterisation tests from the old behaviour first, then port the module and make them pass. The tests are the contract, and without them a migration is a rewrite with no way to know if it worked.
step 03
Review module by module
human review A person reviews each ported module against the original. This is slower than the demo suggests and it is the step that decides whether the result goes to production or to a branch nobody merges.
drop-in
Change one line. Keep your code.
Point the OpenAI SDK, or LangChain, LlamaIndex, your own pipeline, at Helmcode. Same calls, same shapes, private models on EU infrastructure.
read_the_docsfrom openai import OpenAI client = OpenAI( api_key="sk-...", base_url="https://api.helmcode.com/v1", # one line changes ) # 1 · read the module whole, with what it depends on map_ = client.chat.completions.create( model="deepseek-v4-flash", # 1M of context messages=[{ "role": "user", "content": module + headers, }], ) # 2 · characterisation tests first, then port to them tests = client.chat.completions.create( model="deepseek-v4-flash", messages=[{ "role": "user", "content": "Write tests for the current behaviour.", }], )
why here
Why on Helmcode.
What changes when you run it on open models, on infrastructure you control.
Your source never trains a model
Proprietary source is the asset most companies are least willing to paste into a chat window, and rightly. Zero logs means it is not stored, and on-premise means it never leaves the network it lives in.
A migration is thousands of passes
Read, port, fail a test, read again, fix, re-run. That loop over a large codebase is where per-token pricing quietly kills the project halfway. A flat rate makes the loop free to run as many times as it needs.
Code-specialised models when you need them
On dedicated or on-premise plans you can run a code-specialised open model on hardware reserved for you, which is the right call for a long migration where throughput and consistency matter more than breadth.
industries
Where it is used.
The sectors where this case has traction, each with its own page and its own regulation.
// faq
Code migration, answered.
What platform teams ask before pointing a model at the system that runs the business.
Which migrations does this actually help with?
The ones where the target is well documented and the source is verbose: COBOL or RPG to Java, VB6 to .NET, Python 2 to 3, Struts to Spring, stored procedures to application code. The further the target is from anything public, the more the human review carries.
Can it migrate the whole system unattended?
No, and anyone promising that is selling you a rewrite you will not be able to certify. What it does is take the mechanical eighty percent and the archaeology, so your engineers spend their time on the decisions instead of on transcription.
What about the undocumented business rules?
Those are the real deliverable. Extracting them into readable documentation as you go is often worth more than the ported code, because it is the thing that lets the next team change the system at all.
// get started
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