Hosted in Sweden
Requests are processed in memory on servers in Stockholm. Documents and questions are not stored, not logged and never used for training.
Zuse is a decision model like TypeSafe's Jev: it reads a document, picks from the options you set and tells you how sure it is. Point your Jev code at Zuse and it runs, hosted in Sweden.
Or try Zuse in the playground, no account needed.
TYPESAFE_BASE_URL=https://api.typesafe.aiTYPESAFE_BASE_URL=https://api.bineric.comTYPESAFE_API_KEY=your-typesafe-keyTYPESAFE_API_KEY=your-bineric-keyTYPESAFE_DEFAULT_MODEL=jev-latest
Send the request you send Jev today to /v1/systemone. The answer comes back in Jev's format: a choice, a yes/no probability or a score, each with how sure it is.
POST https://api.bineric.com/v1/systemone{"model": "jev-latest","state": "Hi, I was charged twice for my monthly pass last week. I want my money back as soon as possible, this is the second time it has happened!","questions": {"team": {"type": "choice","instructions": "Which team should handle it?","criteria": {"billing": "Payments and refunds","technical": "The app or website is not working","travel": "Questions about routes and departures","other": "Other"}},"refund": {"type": "noul","instructions": "Is the customer asking for a refund?"},"urgency": {"type": "score","instructions": "How urgent is it?","criteria": ["Not urgent","Normal","Urgent","Critical"]}}}
{"model": "zuse-27b","answers": {"team": {"type": "choice","choice": "billing","confidence": 0.9912,"probabilities": {"billing": 0.9912,"technical": 0.0031,"travel": 0.0009,"other": 0.0048}},"refund": {"type": "noul","noul": 0.9874},"urgency": {"type": "score","score": 2.3001,"legend": {"0": "Not urgent","1": "Normal","2": "Urgent","3": "Critical"},"probabilities": {"0": 0.0102,"1": 0.0915,"2": 0.4863,"3": 0.412},"confidence": 0.4863}},"usage": {"input_tokens": 214,"output_tokens": 0},"latency_ms": 18.6}
On Jev's own public English benchmark, Zuse matches its accuracy and can safely automate more decisions. Jev is ahead on general knowledge and reads images.
| Measure | Zuse 27B | Jev (published) |
|---|---|---|
| Decisions safe to automate (errors under 5%) | 77.6% | 70.0% |
| Wrong answers given with high confidence | 1.7% | 3.7% |
| Accuracy | 86.0% | 85.7% |
| Rule-and-evidence reading (SemIf) | 95.6% | 96.5% |
| General-knowledge questions (MMLU-Pro) | about 60% | 84% |
| Image questions | No | Yes |
| Languages | 115 languages, 27 European trained | No list published |
Jev's figures are the ones TypeSafe published. Zuse was scored once on the frozen test split of Jev's public English suites and never tuned on it (21 October 2026). Bold marks the one ahead.
Benchmarks are a start. Your documents are what count. Send the same ones to Jev and Zuse and see where they agree.
import os, requestsAPIS = {"Jev": ("https://api.typesafe.ai", os.environ["TYPESAFE_API_KEY"]),"Zuse": ("https://api.bineric.com", os.environ["BINERIC_API_KEY"]),}QUESTIONS = {"team": {"type": "choice","instructions": "Which team should handle it?","criteria": {"billing": "Payments and refunds","technical": "The app is not working","other": "Other"},}}# One document per paragraphfor doc in open("documents.txt").read().split("\n\n"):got = {}for name, (base, key) in APIS.items():r = requests.post(f"{base}/v1/systemone", timeout=30,headers={"Authorization": f"Bearer {key}"},json={"model": "jev-latest", "state": doc, "questions": QUESTIONS})got[name] = r.json()["answers"]["team"]jev, zuse = got["Jev"], got["Zuse"]same = "same" if jev["choice"] == zuse["choice"] else "DIFFERENT"print(f"{same:9}", f"Jev {jev['choice']} {jev['confidence']:.0%}",f"Zuse {zuse['choice']} {zuse['confidence']:.0%}", doc[:40])
What you need before a decision model can touch customer data.
Requests are processed in memory on servers in Stockholm. Documents and questions are not stored, not logged and never used for training.
A data processing agreement applies automatically, and the list of subprocessors is public. Nothing to negotiate before you start.
Trained on 27 European languages, with the score for every language published, from Norwegian to Maltese.
Reserve a GPU in an EU data centre for your traffic alone, run Zuse in your own AWS account from AWS Marketplace, or on your own servers.
Read the data processing agreement and the list of subprocessors.
No. Zuse is built and trained by Bineric in Norway. No output from Jev was used to train it. It accepts the same request format as Jev so that code written for Jev runs on it.
For choice, yes/no and score questions, yes: set the base URL to https://api.bineric.com and use a Bineric API key. Requests to /v1/systemone that name a Jev model, such as jev-latest, are answered by Zuse 27B. TypeSafe's Python and JavaScript SDKs work as they are. Image (box) questions are not supported.
Yes. Because the requests are the same, you can send the same documents to both and compare the answers on your own data before you decide.
Sign up, add credit and create an API key. There is no waitlist. You can also try Zuse without an account in the playground on the Zuse page.
Get a key, point your code at Bineric, and the answers come straight back.
Jev and TypeSafe are trademarks of their owner. Bineric is not affiliated with TypeSafe.