// use cases · summarization

Long inputs in.
Short answers out.

Summarize calls, tickets, cases and long documents, up to 1M tokens in a single pass.

// how it works

Whatever it is, in one pass.

Transcription, long-context summarization and structured output from a single OpenAI-compatible endpoint, and only inside the EU.

step 01

Ingest the source

whisper-large-v3

Transcripts, tickets, threads or long PDFs, even audio, transcribed first with whisper-large-v3 in 99+ languages. Whatever the source, it's text in.

step 02

Summarize in one pass

deepseek-v4-flash

A 1M-token context window means whole documents and entire call histories go in at once, no lossy chunking, no stitching summaries of summaries.

step 03

Shape the output

qwen3.6

Structured summaries, TL;DR, action items, decisions, sentiment, in the exact format your product or workflow needs, via structured outputs.

// drop-in

Change one line. Keep your stack.

One chat completion, the whole transcript in context. Change the base URL and key and your summarization code runs on private EU models.

read_the_docs

summarize.py

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.helmcode.com/v1",  # one line changes
)

# whole transcript in one pass — up to 1M tokens of context
summary = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "system", "content": "Summarize into a TL;DR and action items."},
        {"role": "user", "content": transcript},
    ],
)

// why helmcode

Summaries that stay private.

The calls and cases you summarize are your most sensitive records, exactly what you shouldn't hand to someone else's model.

Zero logs, by architecture.

The calls, tickets and documents you summarize are never stored, and never train a model, not ours, not anyone's.

Processed in the EU.

Every summary runs on EU infrastructure, not on US hyperscalers subject to the Cloud Act. GDPR and AI Act native.

Whole inputs, one pass.

Up to 1M tokens of context. Summarize a 300-page report or a full call history in a single request, no chunking, no lost detail.

No caps on volume.

Summarize every call and ticket, not just a sample. Limits are RPM and concurrency per key, never total tokens.

Text or audio, one API.

Transcribe with whisper-large-v3 and summarize with an LLM behind a single OpenAI-compatible endpoint, calls become summaries in one place.

In production across

B2B SaaS
Contact center / BPO
Insurance
Healthcare
Pharma & biotech
HR & recruiting

In production at

// summarization faq

Summarization, answered.

What product and operations teams ask before summarizing their own records.

How long an input can I summarize?

Up to a 1M-token context window with deepseek-v4-flash, whole documents, long threads and entire call histories in a single pass, with no lossy chunking.

Can I summarize audio and calls, not just text?

Yes. Transcribe first with whisper-large-v3 (99+ languages) and summarize the transcript with an LLM, both behind the same OpenAI-compatible API.

Do you store what I summarize?

No. Zero logs, your inputs and the summaries produced are never persisted and never train a model.

Can I control the format of the summary?

Yes. Use structured outputs to get a fixed shape, TL;DR, action items, decisions, sentiment, ready to drop into your product or database.

Can it handle high volume?

Yes. There are no token caps, limits are RPM and concurrency per API key, so you can summarize every call and ticket on flat, predictable pricing.

What about sensitive records?

Run on a dedicated GPU or fully on-premise inside your own datacenter, the same API and code, with data that never leaves your network.

// get started

START BURNING TOKENS

Skip the AI infra work. Deploy your first private inference endpoint today.

Flat rate. EU data. OpenAI API compatible.

book_a_call

Long inputs in.Short answers out.