keyvan-ai/Mankei-Reflex

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Mankei Reflex — the German System One decision model

They think. We react.
Typed decisions with calibrated probabilities. One forward pass for the whole request: ~15 ms per decision and ~17 ms for five questions on our inference server, 100 questions in a quarter of a second — on your own hardware. Wire-compatible with the System One API.

_{Recorded on the live demo: the drone judges every obstacle ahead in one pass and the rule in code climbs, avoids or lands. Protocol on the left, latencies as measured.}

Live demo: Reflex Pilot · Live demo: Reflex Browser · mankei.ai · Client examples (GitHub) · Mankei model family

Mankei Reflex is a System One model: a non-autoregressive decision model that takes a state and a set of typed questions and returns typed values with calibrated probabilities — in a single pass, without generating a single token. No prompt to parse, no reasoning prose, no hallucinated text. It runs on-premise, answers in the millisecond range, and speaks German and English.

It is built for the same job as TypeSafe's Jev — routing, guardrails, classification, scoring, agent observability, and real-time decision loops — and it is the first German model of this class, for teams whose data must not leave the building.

At a glance

Model class	System One model · decision model · non-autoregressive · typed outputs
Primitives	Choice (2–255 options, with or without descriptions), Scale (ordered, expected value), Yes/No (probability)
Speed	one forward pass per request — ~15 ms for one decision and ~17 ms for five questions on our inference server (shared data-centre GPU); ~40 ms for four states × three questions in one batch and ~240 ms for 100 questions in one call on a workstation GPU
Calibration	built into every block; ECE 0.031 without post-hoc tuning; coverage 67.5 % at ≤ 1 % error
Languages	German first; English and French domain blocks on request
Interface	HTTP /v1/reflex and the System One wire format /v1/systemone; Python package mankei_decide (in the package)
Deployment	on-premise, GPU (bf16) or CPU; no per-token fees, no data leaving your network
Extension	domain blocks trained by the Mankei Schema Factory — new decision schemas without retraining the core
Licence	Mankei Reflex Evaluation License; production and premium blocks under an enterprise agreement

Why Reflex

The System One category was defined by TypeSafe Jev: a model that decides instead of writing. Reflex takes the same interface and moves it into your own data centre — German first, with domain blocks whose quality is measured before delivery.

	TypeSafe Jev	Mankei Reflex
Weights	closed API	on-premise package, enterprise licence
Where it runs	vendor cloud	your GPU or CPU, air-gapped if you want
Pricing	per-token	no per-token fee
Language	English-centric	German native; English and French blocks for enterprise
New decision schemas	prompt only	domain blocks from the Schema Factory, delivered with measured quality
Wire format	System One	System One, the same request and response shape
Calibration	—	ECE 0.031 in the domain, measured per block, no post-hoc fitting

We do not benchmark competitors. Every figure below is measured on the block shipped in this repository.

Benchmarks

Benchmark	Mankei Reflex	Reference
MASSIVE intent, German, 60 classes, test split, one call, assistant block	78.8 % (ECE 0.088)	60 classes, one call
MASSIVE scenario, German, 18 classes, test split, assistant block	86.6 %	—
Choice questions on unseen option sets, eight domain families, leave-one-schema-out	95.1 %	majority baseline 84.6 %
Accuracy in the domain, all question types	90.5 %	—
Coverage at ≤ 1 % error	67.5 %	—
Latency, one decision / five questions (inference server)	15 ms / 17 ms	one forward pass per request, shared data-centre GPU

Leave-one-schema-out means whole question sets are held out of training and only shown at measurement time — the number a customer sees on a new form, a new option list, a new sensor message.

Speed

Reflex answers a whole request in one forward pass: the state, every question and every candidate are laid out in a single sequence with a tree-shaped attention mask, so no question waits for another and nothing is re-encoded. There are no per-question round trips, no token generation and no cache copies — latency is one GPU pass plus the head.

Request	Inference server (RTX 4000 SFF Ada, shared with a 27B LLM)	Workstation GPU (RTX 4060 class)
one decision	~15 ms	~31 ms
five questions, one pass	~17 ms	~33 ms
browser step, two questions (Reflex Browser, end to end incl. DOM indexing)	~38 ms	—
four states × three questions, one batch (/v1/reflex/batch)	—	~40 ms
~1,000-token state, seven questions	—	~110 ms
100 questions × 6 options, one call	—	~240 ms

Measured end to end with curl against the running server (bf16, block pilot), on our inference server on 23 September 2026 while the same GPU was serving a 27B language model. This is the fastest decision model we know of, and it runs on your own hardware — a real-time loop for vehicles, drones and browser agents that checks many things at once instead of one after the other.

Use cases

• Vehicle and drone decision loops — situation, persons, obstacles, no-fly zones, weather; rules in code, meaning from the model. See Reflex Pilot.
• Browser and portal automation (RPA without selectors) — Reflex reads the page's element table and returns the index to act on. See Reflex Browser.
• Security operations — incident triage, alert routing, CAN-bus and network intrusion classification alongside our IDS models.
• Document workflows — invoice and receipt routing, account assignment, printed-feature checks.
• Customer service routing — 60-way intent and 18-way scenario in one call, calibrated, in German.
• Guardrails and moderation — typed yes/no with a probability you can threshold.
• Agent observability — score every step of an agent trace: on task, risky, needs a human.
• Situation assessment — robotics, control rooms, field reports, in German and English.

How it works

1. State in. JSON or text from your application — a scene, telemetry, a page's element table, a document.
2. One read. The frozen Mankei core reads the state once; every question is answered from that single read. The order of questions and options does not change the answers.
3. Typed answer out. Each question returns its value with a probability; each block carries its own calibration.
4. Your rule on top. Geometry, thresholds, traffic signs, business rules stay in your code — visible line by line in the protocol.

Blocks are trained by the Mankei Schema Factory: generators whose ground truth follows from the modelled world, plus licensed datasets, measured leave-one-schema-out. No chat-model distillation, no synthetic labels from third-party LLMs.

Quickstart

pip install torch transformers safetensors huggingface_hub
huggingface-cli download keyvan-ai/Mankei-Reflex --local-dir mankei-reflex && cd mankei-reflex
python3 -m mankei_decide.server --modell . --kopf bloecke/pilot.safetensors --port 8088

The package contains the core, the engine (mankei_decide/) and the pilot block. States and questions are German — the language the public block is trained on.

curl -s http://127.0.0.1:8088/v1/reflex -H "Content-Type: application/json" -d '{
  "state": {"szene": "Fußgänger 12 m voraus auf der Fahrbahn, quert. Stoppschild in 30 m.", "tempo_kmh": 28, "limit_kmh": 50},
  "questions": {
    "lage":   {"type": "choice", "instructions": "Was ist die Lage vor dem Fahrzeug?",
               "criteria": {"freie Fahrt": null, "Person auf oder neben der Fahrbahn": null, "Hindernis auf der Fahrbahn": null, "Gefahr": null}},
    "person": {"type": "noul", "instructions": "Auf oder neben der Fahrbahn befindet sich eine Person."}
  }}'

from mankei_decide.reflex import Reflex
reflex = Reflex.laden("keyvan-ai/Mankei-Reflex", block="pilot")    # or the local folder
answers = reflex.entscheide(state, questions)      # same state and questions as above, one pass
print(answers["lage"].wahl, answers["lage"].konfidenz, answers["person"].janein)

System One wire format: point SYSTEMONE_URL at http://<host>:8088/v1/systemone; request and response use the System One shape (choice / score / noul).

Blocks

Block	Domain	Availability
pilot	vehicle: situation, persons, hazards, obstacles from sensor sentences	included in the evaluation package
drone	in-flight assessment: obstacles, people, no-fly zones, weather	enterprise
portal	web UI operation from the element table: action, element	enterprise
documents	document type, account, printed features	enterprise
situation	reports from robotics and control rooms, German and English	enterprise
assistant	intent and scenario from user utterances (60 intents, 18 scenarios)	enterprise

Core, engine and the pilot block form the evaluation package. Premium blocks and custom blocks trained on your own states are available on request under an enterprise agreement.

Demos

• Reflex Pilot — a 3D city, a car on the road or a drone on the direct line. Pick the vehicle, set the target on the map; every judgement appears with a timestamp and probability in the protocol next to the scene. https://mankei.ai/reflex/
• Reflex Browser — Reflex operates a German service portal from the DOM element table, no screenshots, no selectors: login, tabs, filters, form, download. A 16-step mission runs without intervention in under ten seconds at ~38 ms per judgement. https://mankei.ai/reflex/browser/

_{On the road: the scene as a German sentence plus the candidate table, five judgements per pass; the car state is about a thousand tokens, so a pass takes longer than the drone's.}

_{Landing approach: the landing circle is checked for vehicles and people before the drone descends; the rule and its reason appear in the protocol.}

_{Reflex Browser, one frame per step: cookies, login, tabs, filters, form, download — every step chosen by Reflex from the page's element table.}

Who is Mankei

Mankei builds German language models from scratch, in Germany, on openly licensed and documented sources, with its own German tokenizer — and runs them where the data lives. The family: Reflex (decisions), the retrieval line (embedder and reranker, Apache 2.0), Mankei-1B-Chat (German chat, CPU-capable), and the dialect line (Bavarian, Swiss German). All on Hugging Face under keyvan-ai; more at mankei.ai.

Licence and access

Mankei Reflex is provided under the Mankei Reflex Evaluation License (LICENSE.md): evaluation, benchmarking and publication of results are permitted. Productive, internal or hosted use, redistribution of the weights and training other models on Reflex outputs require an agreement with Mankei. Access is granted on request via the form above.

Citation

@software{mankei_reflex_2026,
  title  = {Mankei Reflex: a German System One decision model with calibrated probabilities},
  author = {Mankei},
  year   = {2026},
  url    = {https://huggingface.co/keyvan-ai/Mankei-Reflex}
}

Keywords: System One model, decision model, Jev alternative, calibrated probabilities, typed outputs, non-autoregressive, routing, guardrails, intent classification, agent observability, on-premise AI, sovereign AI, German AI model, Entscheidermodell, EU AI Act, real-time decisions, browser automation, autonomous driving demo, drone demo.

_{Mankei · They think. We react.}