See. Hear. Understand. When the answer has a type, Decide.
One unified intelligence. Text, vision, audio, video, code, and system state evaluated into calibrated probabilities across your options — fast when it can be, deep when it needs to be.
Read the Docs →Different computation for different jobs
Language models write. Kai decides.
Ask a writer for a decision and you get prose, then a parser, a retry and a guess at the schema. Ask Kai and the answer is already the type you asked for.
Zen is part of the same system. Reason with Zen; decide with Kai.
Typed answers
Ask in the shape of the answer.
Three question types. Each answer carries its probabilities, so software knows how sure to be.
Sensitivity
See what changes the answer.
A bounded decision has edges. Move the evidence or the risk and watch the answer cross the line — and read the smallest change that would cross it.
Know what would change the answer before you act on it.
Incremental
Only what the evidence touches recomputes.
Switch a source off. The decisions that read it wake and settle again; the rest stay quiet. Compute follows uncertainty.
Example: a scripted scenario, not model output. Switch a source off and only the decisions that read it run again.
Decision Programs
Decisions become programs.
One question becomes many: evidence, code, Kai’s questions, Zen’s prompts, solvers and human approvals, wired into a versioned graph.
The program library ships today. Sending a whole program in one call is being built.Reproducible
Every decision leaves a record.
Each answer names the checkpoint that gave it and a hash of the state it saw. Run the same state again and you know you asked the same question of the same model.
Decision Packages bundle a program’s whole run — inputs, answers, probabilities, policy and result — for replay. The library ships today.Authority
Kai recommends. Policy governs.
A probability is advice. Your policy, written as code, keeps the authority — and a person signs where it says one must.
Results
Kai beside Jev, on the same questions.
Calibration
Confidence you can act on.
“Approve” at .51 and “approve” at .99 pick the same option. Only one should ship without a second look — so a decision model has to be right about how sure it is.
The answer is the distribution. There is nothing to generate.
Agents
Agents need decisions more often than prose.
Most of an agent’s steps are choices among known options. Each is one call, with its probability, and no prose to parse.
Hanzo Dev asks Kai where to fix, what to do next and whether the work is done.One system
Reason with Zen. Decide with Kai.
Neither replaces the other. Zen writes what could be done; Kai says what should be.
Status
What ships today.
The model and the call are live. The decision plane around them is being built — in the open, and marked as such.
API
One endpoint for decisions.
Name the questions, define criteria, pass multimodal state. Kai evaluates the evidence and returns calibrated probabilities for every option — fast by default, deepening compute only when ambiguity requires it.
Kai-1.7 target Set reasoning to "auto" (default), "fast", or "deep". In the SDKs it is post_decisions in Python and postDecisions in TypeScript. Hold a state once with observe, then decide over it by handle.Architecture
Modular encoders. One decision core.
Kai organizes specialized modular encoders into a unified decision engine. Long text (up to 32,768 tokens) and structured telemetry are evaluated natively today; high-resolution imagery, acoustic signals, and video frames project into the unified latent space as the Kai-1.7 target.
The functional parts list:
Use it for
Any question with a known set of answers.
Routing, triage, safety, approvals, agent control. If you can list the options, Kai can weigh them.
Kai · Decision Model
Calibrated decision intelligence. Every parameter, wire protocol, and metric.
Evaluates multimodal evidence into discrete probabilities across typed schemas. 38 ms p50 (one question, production CPU), zero output token cost, and 20 production enterprise recipes.
# "reasoning" is the Kai-1.7 target: api.hanzo.ai refuses the field today (400).
curl -X POST https://api.hanzo.ai/v1/decisions \
-H "Authorization: Bearer $HANZO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kai",
"reasoning": "auto",
"state": { "telemetry": { "bearing_temp_c": 94.2, "rpm": 3600 } },
"questions": {
"triage": {
"type": "choice",
"criteria": { "nominal": "within specs", "inspect": "anomaly", "halt": "critical" }
}
}
}'Enterprise Solutions
Battle-tested decision blueprints across high-throughput enterprise operations.
Edge-to-cloud calibrated decision pipelines built for massive SKU inventories, cold-chain logistics, and automated triage.
Shelf & Ghost Inventory Triage
Multimodal edge vision detects misplaced SKUs, out-of-stock items, and occluded facings. Kai outputs calibrated probabilities over discrete restock actions with zero output tokens.Cross-Dock Pallet Triage
Real-time dock sensors flag pallet damage, tilt, and stretch-wrap tears. Kai computes disposition probabilities under site safety controls and routes to restock or quarantine.High-Value Returns Fraud Inspection
Pairs high-resolution visual anomaly detectors with Kai discrete decision logic to flag item swaps, serial mismatches, tampering, and restock vs scrap dispositions.Refrigeration & Fleet Anomaly Triage
Continuous multivariate telemetry ingestion across trailer temperature, compressor vibration, and RPM sensors. Flags impending unit failure hours before spoilage thresholds trip.Research & Architecture
Deep-dives, ethics benchmarks, and decision model specifications.
Read peer-reviewed research, calibrated benchmark results, and engineering papers from the Kai research team.
Read
The model, the runs, the specification.
One model, the questions it is scored on, and the writing.
Paste and ship
Paste this into any agent. It reads the skill manifest and calls Kai from there.
Read https://hanzo.ai/skill.md and use Hanzo Kai in my project. Start with: hanzo decisions create