use cases · Conversation QA
Quality teams listen to a sample because listening is expensive, and then argue about whether the sample was representative. Transcribe everything, score it against the checklist you already use, and spend the human hours on coaching instead of on listening.
how it works
Three steps, with the component that does the work in each one.
step 01
whisper-large-v3 Open multilingual transcription on your own infrastructure. Keeping agent and customer on separate channels is what makes the scoring possible later, so it is worth getting right at the recording end.
step 02
deepseek-v4-flash Your existing QA form becomes the prompt: greeting, identification, the disclosure you are required to make, the resolution, the closing. Each item comes back with a verdict and the quote that justifies it.
step 03
qwen-3.6 Per-agent and per-queue summaries over the full month rather than over the twelve calls somebody had time for. The pattern that shows up across a thousand conversations is the one worth a training session.
drop-in
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 · transcribe, speakers on separate channels turns = client.audio.transcriptions.create( model="whisper-large-v3", file=call, ) # 2 · score against the QA form you already use scored = client.chat.completions.create( model="deepseek-v4-flash", messages=[ {"role": "system", "content": checklist}, {"role": "user", "content": turns.text}, ], response_format={"type": "json_object"}, )
why here
What changes when you run it on open models, on infrastructure you control.
Sampling exists because human listening costs money, not because two percent is statistically satisfying. Once the cost of listening to everything is flat, the compromise has no reason to survive.
A call recording carries a voice, a name, an account and sometimes a health or financial detail nobody wrote down. Zero logs and EU-only inference keep it out of a third party, and on-premise keeps it out of the network entirely.
No fixed scorecard imposed by a vendor. Your criteria are a prompt you own and can change on Monday, including the regulatory items your own compliance team adds.
industries
The sectors where this case has traction, each with its own page and its own regulation.
// faq
What quality and compliance teams ask before scoring every call instead of a sample.
If the recording keeps them on separate channels, cleanly and always. From a single mixed channel, diarisation is a guess that gets most turns right and some wrong, which is not good enough for scoring somebody performance. Fix it at the recorder if you can.
Monitoring and evaluating the performance of workers is named in Annex III of the AI Act, and an employer has to inform workers and their representatives before putting such a system into service. This is a question for your legal and works-council process, not one infrastructure answers. What it can do is make every score explainable and traceable.
Whisper is strong across Spanish variants and handles code-switching, which matters in Spain and in Latin American operations. Measure it on your own audio before you commit: line quality moves the result more than accent does.
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Flat rate. EU data. OpenAI API compatible.
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