step 01
Index the set
qwen3-embedding Embed reports, tickets and filings so the whole corpus is queryable, not one document at a time. The set becomes searchable, not just storable.
// use cases · document analysis
Find the signal in
thousands of pages.
Analyze reports, tickets and filings across an entire corpus, questions, patterns and anomalies surfaced with citations.
// how it works
Analysis across the whole document set.
Indexing, reasoning and ranking from a single OpenAI-compatible endpoint, over your full corpus, and only inside the EU.
step 02
Analyze & reason
deepseek-v4-flash Ask questions, compare across documents and reason over up to 1M tokens at once, surfacing patterns and anomalies, not just keyword lookups.
step 03
Surface findings
qwen3.6 Get the findings, flags and outliers back, the few things that matter pulled out of thousands of pages, each traceable to its source document.
// drop-in
Change one line. Keep your jobs.
One chat completion, your whole document set in context. Change the base URL and key and your analysis 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 ) # reason across the whole set — up to 1M tokens of context analysis = client.chat.completions.create( model="deepseek-v4-flash", messages=[ {"role": "system", "content": "Analyze the filings. Flag anomalies and cite the source."}, {"role": "user", "content": corpus}, ], )
// why helmcode
Analysis for documents that can't leave.
The corpora worth analyzing, filings, defense, infrastructure, are exactly the ones you can't upload to a US API.
Zero logs, by architecture.
The reports and filings you analyze are never stored, and never train a model, not ours, not anyone's.
Processed in the EU.
Filings, tickets and internal reports stay on EU infrastructure, not on US hyperscalers subject to the Cloud Act. GDPR and AI Act native.
Reason over the whole corpus.
Up to 1M tokens of context. Compare across documents and reason over a full set in one pass, no chunking, no lost cross-references.
No caps, analyze everything.
Run analysis over every report, not a sample. Limits are RPM and concurrency per key, never total tokens.
Runs in closed environments.
Dedicated GPUs or fully air-gapped on-premise, bring the analysis to where the data already lives, with no external connectivity.
Drops into your pipeline.
OpenAI-compatible chat and embeddings. Change the base URL and key; your analysis jobs keep running, unchanged.
In production across
- Banking & fintech
- Defense & public sector
- Telco
- Energy & utilities
// analysis faq
Analysis, answered.
What risk, compliance and engineering teams ask before analyzing their own documents.
How is this different from extraction or summarization?
Extraction pulls fixed fields; summarization condenses a single input. Analysis reasons across a whole set of documents, comparing, spotting patterns and flagging anomalies, and traces each finding back to its source.
How large a document set can it handle?
Up to a 1M-token context window per request with deepseek-v4-flash, and far larger sets by combining it with embeddings and retrieval over your indexed corpus.
Can it run air-gapped or on-premise?
Yes. Run on a dedicated GPU or fully on-premise inside your own datacenter, with no external connectivity, analysis where the data already lives.
Do you store the documents I analyze?
No. Zero logs, your documents and the analysis produced are never persisted and never train a model.
Are findings traceable to the source?
Yes. Ground the analysis with retrieval so every finding cites the exact document and passage it came from, no black-box conclusions.
Can it handle high volume?
Yes. There are no token caps, limits are RPM and concurrency per API key, so you can analyze every report rather than a sample.
// get started
START BURNING TOKENS
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Flat rate. EU data. OpenAI API compatible.