step 01
Bring the documents
qwen3-embedding Pass the document and what to check it against — a prior version, a policy, a checklist or a reference set. Both go in, indexed and ready.
// use cases · document review
Review and compare,
with the receipts.
Compare versions, check clauses against your standards and QA outputs, every finding grounded with a citation.
// how it works
Compare, check, and cite — clause by clause.
Reasoning, comparison and grounding from a single OpenAI-compatible endpoint — every verdict traceable, and only inside the EU.
step 02
Compare & check
deepseek-v4-flash The model compares clause by clause, flags differences, gaps and risks, and grades against your criteria — reasoning, not pattern matching.
step 03
Cite the evidence
qwen3-embedding Every finding links back to the exact source — clause, line or version — grounded with a citation, not a black-box verdict you have to trust blind.
// drop-in
Change one line. Keep your workflow.
One chat completion returns structured findings with citations. Change the base URL and key and your review pipeline runs on private EU models.
read_the_docsfrom openai import OpenAI client = OpenAI( api_key="sk-...", base_url="https://api.helmcode.com/v1", # one line changes ) # compare against a standard — structured findings with citations review = client.chat.completions.create( model="deepseek-v4-flash", messages=[ {"role": "system", "content": "Compare the draft against the policy. Cite every issue."}, {"role": "user", "content": draft}, ], response_format={"type": "json_schema", "json_schema": findings_schema}, )
// why helmcode
Review you can defend.
When a clause or a verdict matters, you need the source — and you can't leak the document to a third-party model to get it.
Zero logs, by architecture.
The contracts and documents you review are never stored, and never train a model — not ours, not anyone's.
Processed in the EU.
Contracts, drafts and reference material stay on EU infrastructure — not on US hyperscalers subject to the Cloud Act. GDPR and AI Act native.
Grounded, with citations.
Every verdict links to the exact source clause or line. No black-box judgement you can't defend in front of a client or a regulator.
No caps on reviews.
Review every contract and every version, not a sample. Limits are RPM and concurrency per key — never total tokens.
Open models, no lock-in.
DeepSeek V4-Flash, Qwen 3.6, Gemma 4. No vendor can deprecate the model behind your reviews or change pricing on you overnight.
Drops into your workflow.
OpenAI-compatible chat and structured outputs. Change the base URL and key; your review and QA pipeline keeps working.
In production across
- Legaltech & law firms
- Dev tools
- Contact center / BPO
// review faq
Review, answered.
What legal, quality and engineering teams ask before reviewing documents with AI.
What can it review?
Contracts and clauses, document versions (diffs), generated outputs, even code or call transcripts for QA — anything where you compare a document against a standard, a prior version or a checklist.
Does it give citations?
Yes. Every finding is grounded with a citation back to the exact clause, line or version — so a reviewer can verify it, not just trust a verdict.
Can it compare two versions of a document?
Yes. It compares clause by clause, surfaces additions, deletions and gaps, and grades each against your criteria.
Can I get structured findings?
Yes. Use structured outputs to get a fixed shape — issue, severity, citation, suggested fix — ready to drop into your review tool.
Do you store the documents I review?
No. Zero logs — documents and the findings produced are never persisted and never train a model.
What about confidential or privileged documents?
Run on a dedicated GPU or fully on-premise inside your own datacenter — the same API and code, with documents that never leave your network.
// get started
START BURNING TOKENS
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Flat rate. EU data. OpenAI API compatible.