← Deemwar decision model · All recipes

What you get

This page says only what the shipped OpenJevX server does today, and every number on it was measured.

One server, one model, on your machine

• One executable (Go) with an HTTP API, a live dashboard on /, Prometheus metrics on /metrics, a health check on /health, and these recipes on /recipes. The Docker image and the release binaries carry the model inside; task build puts it next to the binary.
• One model file: openjevx.w8.onnx, 8-bit, 598,046,561 bytes (598 MB), from the v0.4.0 release.
• It runs on your side. Inference happens in your process, on your CPU (or your GPU with a CUDA ONNX Runtime and "device": "gpu"). We never run your inference and never see your requests.

Measured latency on CPU

Machine: Apple M5 Pro (Apple Silicon, 18 cores), macOS, CPU only ("device": "cpu"), ONNX Runtime 1.22.0, the server built with task build. Other work was running on the machine at the time (load average about 7), so treat these as a busy laptop, not a benchmark rig. 50 sequential requests after 5 warm-up requests, timed with curl -w '%{time_total}' (so the numbers include HTTP):

The loaded server used about 1.6 GB of resident memory. Requests are answered one model run at a time; all the questions in one request go through the model together, so asking three questions in one call is cheaper than three calls.

# the command used, per request body
for i in $(seq 50); do curl -s -o /dev/null -w '%{time_total}\n' localhost:21118/v1/systemone -d @body.json; done | sort -n

Three question types

All three go to POST /v1/systemone as {"state": ..., "questions": {NAME: {type, instructions, criteria}}}. Every answer below is real output from the server described above.

Yes/no: noul

curl -s localhost:21118/v1/systemone -d '{"state": "INVOICE 2026-117  Issued: 2026-09-01\nConsulting services, September .......... 1,250.00 EUR\nTotal due: 1,250.00 EUR\nPayment terms: due within 45 days of the issue date.", "questions": {"claim": {"type": "noul", "instructions": "Does the invoice say the total is 1,500 EUR?"}}}'

{"claim":{"action":{"act_probability":1},"answer_confidence":0.8777,"confidence":0.8777,"noul":0.1223,"probabilities":{"false":0.8777,"true":0.1223},"type":"noul"}}

noul is the probability of yes (here: no). confidence is how sure it is of whichever side it leans to.

Pick one: choice

curl -s localhost:21118/v1/systemone -d '{"state": "I want my money back for order 88", "questions": {"tool": {"type": "choice", "instructions": "Which function should handle this request?", "criteria": {"refund_payment": "refund a charge", "create_invoice": "create a new invoice", "update_email": "change the billing email"}}}}'

{"tool":{"action":{"act_probability":1},"answer_confidence":0.9484,"choice":"refund_payment","confidence":0.9484,"probabilities":{"create_invoice":0.0276,"refund_payment":0.9484,"update_email":0.024},"type":"choice"}}

Rating: score

curl -s localhost:21118/v1/systemone -d '{"state": "Checkout is down, customers are being charged twice", "questions": {"sev": {"type": "score", "instructions": "How severe?", "criteria": ["low", "medium", "high"]}}}'

{"sev":{"action":{"act_probability":1},"answer_confidence":0.7616,"confidence":0.7616,"probabilities":{"0":0.1161,"1":0.1223,"2":0.7616},"score":1.6455,"type":"score"}}

score is the expected level (0 = the first criterion); probabilities are per level. The same question inside a three-question request gives the same answer (Several judgements in one call).

Each block above is the answers object; the full response also carries "model": "openjevx" and "usage": {"input_tokens": N, "output_tokens": 0}.

How good is it

It is a small, general model: on the 18 recipes here it gets most everyday calls right, and it misses some that hosted Jev gets (a due date, a prompt injection, a password in a URL). Each recipe page says how it did. The committed gate numbers are in llmresults/10-v041-baseline.md: on the everyday-basics gate (469 questions worded differently from training) v0.4 scores 66.3% accurate, 46.9% usable (right and past jevx's default thresholds) and 17.9% confidently wrong. Test it on your own questions before you rely on it.

Getting a model fitted to your data

These are ways to get a model trained on your rules and records. They are not features of this binary; the binary serves whichever 8-bit model you point "model" in openjevx.json at.

1. Do it yourself (ADR 0006, accepted): a guide and a step-by-step toolkit from your documents and records to labelled questions, a leakage check, training, the gate, and an 8-bit model you serve with this server.
2. We train it for you (ADR 0007, proposed): you bring documents and records, we do the data work and train on our GPUs, you confirm every rule, and you get the 8-bit model, its gate report and the trainable checkpoint. You run it yourself; we never serve your traffic.
3. Inside your own cloud (ADR 0008, proposed): an appliance from a cloud marketplace with the server and a training UI, so your data never leaves your account.

The ADRs are in docs/adr/ in the repository.