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What if AI could add judgment without taking control away from
your software?

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That is the idea behind Jev Flow: clear inputs, typed judgments,
and routes the application can inspect.

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Deterministic AI does not promise a perfect model. It means the
workflow rules are explicit, validated, and owned by code.

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Jev answers a bounded question with a Choice, a probability, or a
score. Code validates the shape before any branch runs.

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And Laya? It offers an optional local inference path for
compatible models when a team prefers to run its own checkpoint.

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In the Flow Studio, connect triggers, Jev judgments, conditions,
and logs on one visual canvas.

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This support-triage walkthrough uses a labeled fixture. It
demonstrates a route without presenting fixture data as a provider

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call.

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The Compendium generates and certifies 388,080 configurations for
validity and unique identifiers. That is catalog coverage, not

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model evaluations.

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Now the Battle Arena sends the same synthetic incident live to
TypeSafe Jev and the selected paid OpenRouter model.

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This captured run returned five typed Jev judgments in 740
milliseconds; GPT-4.1 Mini answered in 1,638 milliseconds. One run

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is a demonstration, not a benchmark.

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The games make rules tangible. Tic-tac-toe uses a local minimax
opponent, so its move needs no model or API call.

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In Chess, local rules reject illegal moves. In Blast Garden and
City, inspect how each state changes as you play.

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The Self-Driving Sim sends structured lane, speed, and obstacle
state to a decision step. This demo uses no camera vision or

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OpenCV.

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Inspect the trace, change a flow, and test it with your own
synthetic cases. Jev Flow makes model-assisted workflows visible

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and auditable.
