Use case
Document analysis & extraction
Analyze long documents — contracts, reports, records — in a single pass on a 320K-context model, in the region the document belongs to.
The problem
Long documents rarely fit the small context windows that force naive chunking, and chunking degrades extraction quality across cross-references and long clauses.
Why data control matters
Contracts, medical and financial records carry the strictest handling rules. Region-pinned inference with no storage by default, no sharing and no training on shared or third-party models keeps the document inside the boundary your own rules require, instead of a provider's default.
Architecture summary
Read the document, send it with the task to chat completions, parse the returned answer. No intermediate store is needed on our side — the session is stateless.
Model strategy
A single Qwen 3.8 call with the full document and the extraction instruction is often enough — the 320K-token context was chosen for exactly this shape of workload.
Regional considerations
Match the document's legal home: EU documents in the EU market (operated from Germany), records from the USA in the USA market, and LATAM documents in the LATAM market (operated from Colombia).
Governance & data boundary
Prompts and outputs are not stored by default, not shared, not trained on; processing stays in the market's operating location. Access is per-key with fair-use limits.
Integration example
response = client.chat.completions.create(
model="qwen-3-8",
messages=[
{"role": "user",
"content": f"Extract the payment terms from:\n{contract}"},
],
)Frequently asked questions
How long can the documents be?
The context window is 320K tokens, so long documents can be analyzed in one pass instead of being split into losing chunks.
Is the document retained after the call?
No. The inference is stateless: nothing from the call is stored on our side, shared, or used for training.
Start with control
Join the waitlist for an account in your region.