diff --git a/docs/content/advanced/model-configuration.md b/docs/content/advanced/model-configuration.md
index 5cfa74ccd..10e71ef96 100644
--- a/docs/content/advanced/model-configuration.md
+++ b/docs/content/advanced/model-configuration.md
@@ -1066,7 +1066,9 @@ known_usecases:
   - embeddings
 ```
 
-Available flags: `chat`, `completion`, `edit`, `embeddings`, `rerank`, `image`, `transcript`, `tts`, `sound_generation`, `tokenize`, `vad`, `video`, `detection`, `llm` (combination of CHAT, COMPLETION, EDIT).
+Available flags: `chat`, `completion`, `edit`, `embeddings`, `rerank`, `image`, `transcript`, `tts`, `sound_generation`, `tokenize`, `vad`, `video`, `detection`, `score`, `token_classify`, `systemone`, `llm` (combination of CHAT, COMPLETION, EDIT).
+
+`systemone` marks a model as a decision model for the [SystemOne API]({{% relref "features/systemone" %}}) (`POST /v1/systemone`). It is never guessed, and a model that declares it is not listed as a chat, completion or embeddings model.
 
 `token_classify` marks a model as a token-classification (NER) provider for the PII filter (e.g. an `openai-privacy-filter` GGUF). Declare it explicitly together with `embeddings: true` (the classifier loads via TOKEN_CLS pooling). It runs on the dedicated `privacy-filter` backend (`backend/cpp/privacy-filter`), a standalone GGML engine for the `openai-privacy-filter` family - separate from `llama-cpp`, which no longer carries the token-classification path.
 
diff --git a/docs/content/features/systemone.md b/docs/content/features/systemone.md
new file mode 100644
index 000000000..d58a3d436
--- /dev/null
+++ b/docs/content/features/systemone.md
@@ -0,0 +1,103 @@
++++
+disableToc = false
+title = "SystemOne decisions"
+weight = 66
+url = "/features/systemone/"
++++
+
+SystemOne is an API for fast, typed decisions. You send a piece of text (the
+*state*) and a set of named questions. A decision model answers each question
+with a value and a confidence, in one pass. The model does not generate text, so
+there is nothing to parse and no free-form output to validate.
+
+The request and response shapes follow the [kev](https://github.com/jaredpalmer/kev)
+project and match the `/v1/systemone` endpoint that Ollama added in 0.35.
+
+## Endpoints
+
+| Endpoint | Method | Description |
+|---|---|---|
+| `/v1/systemone` | POST | Answer all questions in one pass |
+| `/v1/systemone/permute` | POST | Re-run one choice question under `n_perm` option orders |
+| `/v1/systemone/separate` | POST | Answer each question in its own pass |
+
+## Question types
+
+| Type | Answer | Fields in the answer |
+|---|---|---|
+| `choice` | One option out of a named set | `choice`, `probabilities`, `confidence` |
+| `noul` | Yes, no or unknown for a statement | `noul` (0 to 1), `entities` |
+| `score` | One level on a scale | `score`, `legend`, `probabilities`, `confidence` |
+
+## Example
+
+```bash
+curl http://localhost:8080/v1/systemone -H "Content-Type: application/json" -d '{
+  "model": "laya-vllm-cpp",
+  "state": "My order arrived broken and I want my money back. This is the second time.",
+  "questions": {
+    "team": {
+      "type": "choice",
+      "instructions": "Which team should handle this ticket?",
+      "criteria": {
+        "billing": "Payments, invoices and refunds",
+        "shipping": "Delivery and damaged goods",
+        "product": "Questions about how the product works"
+      }
+    },
+    "refund_requested": {
+      "type": "noul",
+      "instructions": "The customer explicitly asks for a refund"
+    },
+    "urgency": {
+      "type": "score",
+      "instructions": "How urgent is this ticket?",
+      "criteria": ["not urgent", "somewhat urgent", "urgent", "critical"]
+    }
+  }
+}'
+```
+
+Every answer carries a `confidence` value, and the response reports token usage
+and `latency_ms`.
+
+## Choosing a model
+
+A model can serve SystemOne only if it is a decision model. Declare the usecase
+in the model config:
+
+```yaml
+name: laya
+backend: vllm-cpp
+known_usecases:
+  - systemone
+parameters:
+  model: convaiinnovations/laya
+```
+
+`systemone` is never guessed, and a model that declares it is not listed as a
+chat, completion or embeddings model. A model that declares usecases without
+`systemone` or `token_classify` gets a `400` from these endpoints that names the
+missing usecase. A config that declares no usecases at all keeps working, so
+setups that predate the flag are not broken. Models that declare `token_classify`
+are served by the zero-shot NER path.
+
+Install one from the gallery and filter on the `systemone` tag:
+
+| Gallery entry | Model | Notes |
+|---|---|---|
+| `laya-vllm-cpp` | Laya | ModernBERT-large, non-autoregressive, about 800 MB |
+| `gliner25-decide-vllm-cpp` | GLiNER2.5-Decide | DeBERTa-v3-large with a classification head, about 2 GB |
+
+The engine, [vllm.cpp]({{% relref "features/vllm-cpp" %}}), also supports the
+kev, CLM and xor decision models. Those checkpoints need a conversion step, so
+they are not gallery entries yet.
+
+Tev1 is an autoregressive decision model. It answers through chat completions
+and does not serve `/v1/systemone` yet.
+
+## Access control
+
+When authentication is on, the three routes need the `systemone` feature. It is
+on by default for every user, like the other API features, and an administrator
+can turn it off per user.
diff --git a/docs/content/features/vllm-cpp.md b/docs/content/features/vllm-cpp.md
index 74e3c0d38..6dc67c0b9 100644
--- a/docs/content/features/vllm-cpp.md
+++ b/docs/content/features/vllm-cpp.md
@@ -160,22 +160,23 @@ forward, which is the required contract for pooling models in vllm.cpp. A
 device-resident forward is tracked as a performance optimization, not a
 correctness gap.
 
-### SystemOne structured-extraction API
+### SystemOne decision API
 
-The `vllm-cpp` backend also exposes kev-compatible SystemOne endpoints that
-turn zero-shot NER into structured question answering. These mirror the API
-from the [kev](https://github.com/jaredpalmer/kev) project:
+The `vllm-cpp` backend serves the kev-compatible SystemOne endpoints: typed
+`choice`, `noul` and `score` questions over a state text, answered by a
+non-generative decision model in one pass. A decision model declares
+`known_usecases: [systemone]`. See [SystemOne decisions]({{% relref "features/systemone" %}})
+for the request shape, the models you can install and the access rules.
 
 | Endpoint | Method | Description |
 |---|---|---|
-| `/v1/systemone` | POST | Answer all questions in one NER pass |
+| `/v1/systemone` | POST | Answer all questions in one pass |
 | `/v1/systemone/permute` | POST | Re-run one choice question under n_perm option orders |
-| `/v1/systemone/separate` | POST | Answer each question in its own NER pass (N passes) |
+| `/v1/systemone/separate` | POST | Answer each question in its own pass (N passes) |
 
-Each question has a `type` of `noul` (binary entity presence), `choice` (pick
-one option), or `score` (pick one level). The `model` field in the request body
-selects the NER model. Labels are derived from the question definition, so no
-`ner_labels` configuration is needed for these endpoints.
+The GLiNER2.5 zero-shot NER model (`token_classify`) also serves these
+endpoints. It derives its NER labels from the question definitions, so no
+`ner_labels` configuration is needed.
 
 ## Beyond text generation