Doze: teach it once. Let the AI sleep.

说明与用途

Doze 使用本地 Gemma 模型一次性学习支付导出与银行导出之间的列映射，保存为 JSON 并在后续处理中重复使用，无需再次调用模型，执行精确算术检查并标记差异。

问题

Recurring AI workflows repeatedly call the model to reconcile payment exports with bank exports, wasting inference resources and time.

结果

Doze achieves 52‑week replay with 41 clean, 11 held for review, and 19 explicit exceptions, all without any model calls during replay. Eight fixed mapping tests pass, and the system flags a one‑cent discrepancy as a mismatch.

工作流

• Teach the model once with headers and human‑approved explanations
• Gemma returns a mapping or refusal
• Human reviews and approves the mapping
• Save the mapping as JSON
• For each new export, load the approved mapping
• Python reconciles payments and bank data using exact arithmetic
• Flag discrepancies or approve clean weeks
• Generate audit ZIP with reconciliation CSV, audit JSON, mapping, and hashes
• Optionally run via CLI without browser or inference

证据

Doze refuses to close a week that is one cent short. It can do that with inference disabled.

来源

The chore is reconciling payment exports against bank exports.

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Doze uses local Gemma to learn the column mapping, then saves it as a small, reviewable JSON artifact.

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The model does not read the transaction rows or decide that a discrepancy is probably fine.

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With inference disabled, the same mapping checks 52 generated weeks: 41 clean, 11 held for review, 19 explicit exceptions. Zero model calls during replay.

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