# Hanzo Kai — Decision intelligence > Kai is a multimodal decision model that turns heterogeneous evidence into calibrated, typed, mutually consistent decisions, resolved in parallel. Kai 1 · decisions, not completions. Kai # Decision intelligence.Not another language model. Kai takes explicit state and a listed question and returns a choice. The coding agent, Hanzo Bot, and Hanzo Team call it for the model, the context, the tools, and the stop. [Explore the research](#research)[View benchmarks](#benchmarks) kai · multimodal · typed · calibrated · parallel · self-hostable vehicle.readiness@3example · scripted evidencecamera CAN audio SysML simulation replay Example: a scripted scenario, not model output. Switch a source off and only the decisions that read it run again. Bounded answers ## Language models reason. Kai decides. Generative models are good at open-ended work. Most decisions inside a real system are not open-ended. - Which tool should run? - Which component failed? - Is the evidence sufficient? - How severe is the risk? - Which option satisfies the constraints? - Should execution continue? - Does a human need to approve this? Each has a bounded answer space. Kai is built for them. hanzo decision zenopen-ended reasoning and generationkaibounded judgment and decision stateembedretrievaltsforecastingvisionperceptionaudiospeech and acoustic perceptiongraphstructural evidencesolvermathematical correctnesspolicyauthority hanzo decision composes all of them Evidence ## Everything is evidence. Text. Code. Images. Video. Audio. Sensors. Telemetry. Time series. Graphs. SysML. Simulation. One decision plane over all of it. evidence plane - text - code - image - video - audio - radar - lidar - thermal - sensors - telemetry - time series - graph - sysml - simulation - documents ↓ modality encodersshared evidence↓kai A decision should not care whether its evidence started as prose, a thermal image or a CAN frame. Specialized encoders produce the evidence, and Kai reasons over one shared representation of it. New sensor tomorrow? Add an adapter. The Decision Program does not change. Sensors ## Sensors are first-class. Kai does not treat telemetry as text. A measurement carries its meaning with it. - CAN / J1939 - IMU - accelerometer - gyroscope - temperature - pressure - voltage - current - vibration - GNSS - radar - LiDAR - thermal - RF - acoustics - network telemetry - industrial buses - custom sensors A number without provenance is just a number. Kai keeps the evidence attached. measurementexample value104.2unit°Cchannelcoolant.temperaturesample_rate100 Hztimestamp11:42:13.428uncertainty±0.4 °Ccalibrationvalidsourcevehicle-17provenancesigned stream Change ## Decide on what changed. A sensor stream can hold millions of samples. A decision should not have to reread all of them. Specialized temporal models turn raw streams into evidence. Kai decides what that evidence means for the system. coolant.temperature · summaryexample current104 °Ctrendrisingderivative+1.8 °C/minbaseline+4.2σforecastlimit crossed in 6 minqualityvalid Parallel ## Entire decisions, in parallel. Agent loops serialize control: a question, a model, an answer, and again. Kai is designed around a different primitive. agent loop question → model → answer ↓ question → model → answer ↓ question → model → answer one call per decision, in series decision programexample faultbearing.91 severitycritical.86 readinessno-go.96 actioninspect-bearing.83 next_testspectrum.88 evidence_sufficienttrue.81 human_reviewyes.99 A Decision Program holds many typed variables, and Kai predicts their distributions together. One state. One shared encoding. Many decisions. Refinement ## Resolve certainty first. Kai borrows from masked diffusion without pretending decisions are text tokens. It starts from an incomplete decision state and predicts every unresolved variable in parallel. Confident nodes freeze. Only the uncertain or mutually dependent are refined. Compute follows uncertainty, rather than revisiting everything. refinementexample start [mask] [mask] [mask] … every node 100 decision nodes pass 187 resolved pass 211 resolved constraint pass2 conflicts resolved Dependencies ## Dependencies are explicit. Fully parallel does not mean independently wrong. A Decision Program is a graph. Independent branches resolve together. Dependent branches resolve when the evidence they need is available. The depth of inference follows the dependency graph, not the number of decisions. A ten-thousand-node program does not mean ten thousand serial model calls. program graph fault ─────┬──▶ action └──▶ readiness severity ─────▶ human_review Types ## Decisions are typed. Kai does not write a paragraph and leave software to work out what it meant. choiceexample Which subsystem is responsible? bearing.91gearbox.05unknown.04 scoreexample Operational risk 0.011.022.063.184.515.22 predicateexample Is human approval required? yes.97no.03 deferexample When evidence does not support a safe decision, not deciding is an answer. decision_readyfalsereasoninsufficient evidencerequired_evidencevibration-spectrum Scale ## A million choices should still be a choice. Flat classifiers degrade when every option has to fit inside one prompt. Kai separates discovery from judgment. Candidate representations compile once with a Decision Program and are reused. That makes large tool catalogs, repositories, knowledge graphs and acquisition alternatives tractable, without showing every option to a frontier language model. large choiceexample 1,000,000 candidates ↓ semantic retrieval top 64 ↓ kai reranking top 8 ↓ joint refinement 1 Specialists ## One model should not pretend to be every model. Kai is the decision layer. Specialists remain specialists. A forecasting model forecasts. A vision model perceives. A solver satisfies hard constraints. A policy engine determines what is permitted. Kai decides across their evidence. hanzo decision hanzo decision └─ kai decision state ├─ zen reasoning ├─ ts forecasting ├─ graph structure ├─ vision perception ├─ audio speech · signal └─ solver math policy authority Package ## From perception to a Decision Package. Consider a vehicle under evaluation. vehicle-17 · evaluationexample - camera inspection - thermal video - radar - CAN telemetry - vibration - operator audio - maintenance history - SysML architecture - simulation results - requirements ↓decision program↓kai↓policy + solver↓human approval↓decision package The result is not just an answer. It is a reproducible record of how the answer was reached. Programs ## Decision-as-a-program. A Decision Program makes the structure explicit. A program can combine deterministic calculations, Kai judgments, Zen reasoning, graph queries, simulations, forecasts, formal solvers and human approvals. The model is part of the program. It is not the program. power-system-selection.yamlexample decision: power-system-selection version: 7 objectives: - mission_range - reliability - maintainability options: - architecture_a - architecture_b - architecture_c constraints: max_mass: 480kg delivery: <= 18 months assumptions: supplier_lead_time: 22 weeks risks: - battery_availability - thermal_margin approvals: - engineering - acquisition Sensitivity ## Ask what flips the decision. A useful decision system tells you more than what it prefers. It tells you where that preference stops being true. Kai and Hanzo Decision run sensitivity and counterfactual evaluation against the same versioned program. No new prompt. No hand-rewritten trade study. power-system-selection · sensitivityexample baselineArchitecture Abattery cost +11%A remains preferredbattery cost +17%A → Bmass limit −4%A remains preferredmass limit −8%A → C Versions ## Decisions that live longer than a meeting. Evidence changes. Requirements change. Suppliers change. Models change. The Decision Program remains. Hanzo records what evidence changed, which assumptions changed, which nodes recomputed, which boundary was crossed, why the result changed and who approved the new one. A trade study becomes a decision system you can refresh. power-system-selection · historyexample version 14 Architecture A ↓ new reliability evidence version 15 Architecture A ↓ mass requirement changed version 16 Architecture B Replay ## Replay it. Every Decision Package can preserve: - program version - input snapshots - evidence hashes - provenance - model revisions - Kai checkpoint - calibration revision - solver state - policy result - probability distributions - human overrides - approval state - execution trace The question is not “What did the AI say?” It is “Can we reproduce the decision?” Business ## Autopilot for business. Most of a business’s work is bounded decisions, followed by generation and execution. Kai decides who, what, when and through which channel: enrichment, lead fit and intent, next action, offer, cadence, human handoff, and when to stop. It personalizes recommendations over the product graph. Zen writes the message. Enso runs the workflow and routes each step to Kai, or to Zen when generation is the work or Kai defers. Policy decides whether contact is permitted: consent, opt-outs, caps, quiet hours and jurisdiction. Configured natively in Hanzo Cloud, with signals from Hanzo Analytics, Insights and Commerce. Each program (marketing.*, sales.*, commerce.*) is measured by its own outcomes and tested in shadow before it acts. [Analytics](https://hanzo.ai/analytics)[Insights](https://hanzo.ai/insights)[Commerce](https://hanzo.ai/commerce) sales.lead@4example sales.lead@4 → intent4fit5timingnownext_actiondemo Agents ## Built for agents. The same primitive controls Hanzo’s own agent runtime. Generative reasoning is reserved for the work that actually requires generation. Control becomes a typed system. agent runtime · kai decides which model? which tool? which file? which skill? how much context? does this touch secrets? should execution continue? did the agent make progress? does a human need to approve? Physical systems ## Built for physical systems. ### Vehicles Readiness, diagnostics, trade studies and maintenance. ### Robotics Perception-conditioned actions and safety escalation. ### Manufacturing Process state, anomalies, inspection and intervention. ### Aerospace Telemetry, digital engineering and configuration decisions. ### Energy Forecasting, equipment state and operational decisions. ### Infrastructure Sensors, simulations, risk and long-horizon planning. One decision abstraction. Different evidence. Deployment ## Built to run where the evidence lives. Decision evidence does not need to leave the infrastructure that owns it. Kai extends Hanzo’s operating plane with decision intelligence. - Hanzo Cloud - Your cloud - Your Kubernetes - Your GPU - On-prem - Disconnected environments Results ## Kai, against Laya and Jev. The top mean accuracy across 11 application suites. [85.2% ### Mean accuracy, 11 application suites Jev 77.0% · Laya 70.2%](#kai-harness)[7 of 12 ### Suites won outright DAIR Emotion, Banking77, Phishing, Toxicity, RAG relevance, Model routing, MASSIVE, 51 languages](#kai-harness)[7 · 1 · 4 ### Suites won, tied, behind behind by points on AG News -0.7, Support triage -6.0, Jailbreak -2.0, Typed decisions -0.7](#kai-harness)[51 of 51 ### MASSIVE languages above Laya macro accuracy 90.9% against 38.2%](#kai-languages) Laya is the upstream kai-1 weights, served natively. Jev is typesafe/jev-1.13-20260917 through OpenRouter. the frozen decision harness Accuracy Calibration error (ECE) Suite Kai Laya Jev Kai Laya Jev AG News 94.3% 95.0% 86.0% 0.034 0.032 0.102 DAIR Emotion 93.5% 59.5% 60.3% 0.019 0.306 0.280 Banking77 90.8% 42.5% 83.5% 0.076 0.381 0.073 Support triage 44.3% 50.2% 36.5% 0.441 0.091 0.483 Email spam 99.8% 99.8% 97.8% 0.003 0.011 0.060 Phishing 99.0% 98.3% 90.0% 0.010 0.010 0.039 Jailbreak 92.0% 70.5% 94.0% 0.072 0.262 0.047 Toxicity 80.3% 53.0% 66.3% 0.191 0.297 0.177 RAG relevance 67.5% 62.5% 62.0% 0.029 0.117 0.279 Model routing 100.0% 63.9% 97.7% 0.000 0.089 0.014 Typed decisions 75.9% 76.6% 73.6% 0.172 0.213 0.047 MASSIVE, 51 languages 90.9% 38.2% 89.0% 0.060 0.418 0.064 Mean, 11 suites 85.2% 70.2% 77.0% 0.095 0.164 0.145 11 application suites and MASSIVE intent in 51 languages, the same questions to all three. Laya answers typed decisions on the checkpoint it ships for them. Source: [api.hanzo.ai/v1/research/runs?org=hanzo · harness · Kai a7@0834a74f · harness a1a4a73](https://github.com/hanzoai/benchmarks/tree/a1a4a73d524cecf8038e30c408ad30b66e71d490/decision) Every language massive intent, by language Accuracy Language Kai Laya Jev Afrikaans 91.0% 34.0% 87.0% Amharic 85.0% 15.0% 85.0% Arabic 92.0% 46.0% 87.0% Azerbaijani 94.0% 36.0% 88.0% Bangla 92.0% 45.0% 86.0% Welsh 86.0% 16.0% 76.0% Danish 92.0% 46.0% 88.0% German 91.0% 53.0% 93.9% Greek 94.0% 44.0% 94.0% English 94.0% 82.0% 92.0% Spanish 92.0% 53.0% 93.0% Persian 94.0% 51.0% 95.0% Finnish 92.0% 30.0% 89.0% French 91.0% 60.0% 90.0% Hebrew 91.0% 37.0% 94.0% Hindi 91.0% 46.0% 95.0% Hungarian 90.0% 34.0% 92.0% Armenian 89.0% 25.0% 84.0% Indonesian 96.0% 36.0% 93.0% Icelandic 85.0% 33.0% 84.0% Italian 91.0% 47.0% 96.0% Japanese 97.0% 64.0% 97.0% Javanese 89.0% 16.0% 75.0% Georgian 83.0% 15.0% 88.0% Khmer 84.0% 20.0% 81.0% Kannada 91.0% 30.0% 87.0% Korean 91.0% 47.0% 94.0% Latvian 89.0% 30.0% 85.0% Malayalam 87.0% 28.0% 91.0% Mongolian 88.0% 16.0% 84.0% Malay 93.0% 27.0% 92.0% Burmese 92.0% 16.0% 89.0% Norwegian Bokmål 94.0% 50.0% 86.0% Dutch 92.0% 40.0% 90.0% Polish 94.0% 47.0% 92.0% Portuguese 93.0% 44.0% 90.0% Romanian 91.0% 37.0% 94.0% Russian 97.0% 57.0% 94.0% Slovenian 91.0% 33.0% 89.0% Albanian 93.0% 29.0% 76.0% Swedish 93.0% 48.0% 94.0% Swahili 84.0% 13.0% 70.0% Tamil 88.0% 31.0% 92.0% Telugu 87.0% 22.0% 91.0% Thai 93.0% 48.0% 94.0% Filipino 91.0% 30.0% 85.0% Turkish 90.0% 41.0% 92.0% Urdu 90.0% 42.0% 90.0% Vietnamese 91.0% 33.0% 89.0% Chinese (China) 94.0% 65.0% 94.0% Chinese (Taiwan) 95.0% 61.0% 93.0% Twenty intents to choose from, a hundred utterances per language. Source: [api.hanzo.ai/v1/research/runs?org=hanzo · harness · Kai a7@0834a74f · harness a1a4a73](https://github.com/hanzoai/benchmarks/tree/a1a4a73d524cecf8038e30c408ad30b66e71d490/decision) what a decision model can do Measure Kai Laya Jev Choosing among many options Accuracy, 4 options – 77.8% 90.5% Accuracy, 16 options – 67.0% 85.3% Accuracy, 77 options – 42.5% 84.3% Accuracy, 150 options – 61.0% 94.0% Accuracy, 1,000 options – 0.0% 0.0% Accuracy, 10,000 options – 0.0% 0.0% Accuracy, 100,000 options – 0.0% 0.0% Refused, 1,000 options – 100.0% 100.0% Refused, 10,000 options – 100.0% 100.0% Refused, 100,000 options – 100.0% 100.0% Five dependent questions on one state Accuracy – 33.6% 83.7% All five right – 0.5% 43.8% Answers that break a rule – 63.7% 24.8% Accuracy, projected onto the rules – 32.8% 87.9% All five right, projected – 4.0% 56.0% Invariance Answer changes when the options are reordered – 25.5% 6.0% Largest probability shift under reordering – 0.996 0.470 A yes/no with its sides swapped: answer mirrors – 20.5% 6.3% A yes/no negated: answer mirrors – 30.0% 88.3% Sensor evidence, accuracy Human activity, UCI HAR – 20.3% 15.3% Turbofan remaining life, C-MAPSS – 17.0% 30.0% Valve sound, MIMII – 50.0% 84.0% Conformal prediction sets at a 90% target Coverage – 90.1% 90.1% Mean set size – 6.801 1.304 Programs and deployment Questions re-asked after one evidence change – 100 of 100 100 of 100 Runs offline – yes no Self-hosted – yes no Evidence leaves the machine – no yes Pinned by – weights sha256 model name The same cases to all three. Laya and Jev answer each question on its own; Kai refines dependent questions over three passes. Kai a7@0834a74f has no capability run yet, so no Kai figure shows here. Source: [api.hanzo.ai/v1/research/runs?org=hanzo · capability · Kai a7@0834a74f · harness a1a4a73](https://github.com/hanzoai/benchmarks/tree/a1a4a73d524cecf8038e30c408ad30b66e71d490/decision) latency of one decision Serving mode Device Dtype p50 ms p95 ms p99 ms Cold encode ROCm BF16 19.8 29.0 – Incremental encode – – – – – Cached decision – – – – – Kai a7@0834a74f, one decision a call, timed on a production accelerator. A mode with no CUDA or ROCm measurement shows as absent; nothing here is projected. Source: [api.hanzo.ai/v1/research/runs?org=hanzo · latency · Kai a7@0834a74f · harness a1a4a73](https://github.com/hanzoai/benchmarks/tree/a1a4a73d524cecf8038e30c408ad30b66e71d490/decision) Research ## Research, not magic. Kai is benchmarked against the systems it descends from and the ones it competes with, on the same questions, and every table above reproduces from the harness commit under it. research tracks - 01Extreme-cardinality decisionsFrom tens of candidates to hundreds of thousands. - 02Multilingual decision intelligenceConsistent decisions across languages, rather than translation-first inference. - 03Shared-state decodingEncode the evidence once and resolve many questions against it. - 04Decision diffusionParallel structured inference with selective refinement. - 05Multimodal evidenceSensors, video, audio, time series, graphs and engineering models. - 06Calibration and abstentionKnowing when the evidence is insufficient. - 07Decision graph consistencyMaking many locally plausible decisions globally coherent. [Read the specification, HIP-1332](https://github.com/hanzoai/hips/blob/main/HIPs/hip-1332-kai-the-decision-model.md) Kai + Zen ## They solve different problems. generation · decision zenkaiWhat should the implementation look like?Which implementation satisfies these constraints? Explain the evidence.Is the evidence sufficient? Generate a test plan.Which test should execute next? Reason about the alternatives.Which decision state follows? Generation and decision are complementary computational primitives. They should not be forced into the same model call. The stack ## Zen generates. Enso routes. Kai decides. - routesEnsoA Hanzo AI original · proprietaryEnso 1 is the agentic model with the learned router. On every call — text, vision, audio, video, sensors, and the rest — it picks the kind and the type: Zen 6 to write, Kai 1 to decide, or another open or closed frontier model.one route for every modalityExplore Enso → - generatesZenZen LM · Zoo Labs Foundation · open weightsZen LM is from Zoo Labs Foundation, a 501(c)⁠(3) non-profit: open-weight models for agentic coding on your own machine and for marketing work, across text, vision, audio, and video. Self-host it, or run it on Hanzo Cloud.Zen 6 and Zen 6 Flash now · Zen 7 research previewExplore Zen → - decidesKaiA Hanzo AI original · proprietaryThe decision and control models. Given explicit state and a listed question, Kai 1 returns a calibrated choice, score, or abstention. The coding agent calls it for the model, the context, the tools, the reasoning budget, and the stop. Hanzo Bot and Hanzo Team call it the same way.more Kai, less latency, less spendThis page Policy governs. A model’s verdict can tighten a call and never loosen it. API ## Kai is exposed through Hanzo Decision. One endpoint, POST /v1/decisions: a program, the evidence it reads, and typed answers back. Kai returns a choice, not a completion. The example below is the shape of the call. POST /v1/decisionsexample POST /v1/decisions { "model": "kai", "program": "vehicle.readiness@3", "evidence": [ "sensor://vehicle-17/can", "video://inspection-382", "sysml://vehicle-17", "graph://maintenance-kg" ] } → { "decision": "dec_01…", "answers": { "readiness": { "value": "no-go", "probability": 0.96 }, "human_review": { "value": true, "probability": 0.99 } } } Open ## Open research. Open runtime. Kai builds on open research in typed decision models and evolves it into Hanzo’s native multimodal Decision runtime. Weights, benchmarks and runtime components are published as they reach reproducible release gates. Run Kai through Hanzo Cloud. Run it on your own infrastructure. Study the benchmark. Fork the runtime. ## Stop asking one model to do everything. Reason with Zen. Perceive with specialist models. Forecast with temporal models. Verify with solvers. Govern with policy. Decide with Kai. One decision plane over every kind of evidence. [Try Hanzo](https://hanzo.ai/login)[Explore Hanzo Decision](#api)