# TypeSafe AI - Jev

OpenAITools.app のテレメトリ（2026-11-10 検証）によると、TypeSafe AI - Jev（typesafe.ai）は AIモデル（世界ランキング #2）に分類される AI プラットフォームで、月間 614.87万の訪問を生み出し、前月比のトラフィック増減は -351.3万（-36.4%）です。主要な地域市場はアメリカ合衆国（トラフィックシェア 26.4%）で、有料の料金モデルで運営されています。月間ボリューム別の主な競合代替ツールには Jev AI（月間 2.96万訪問）、RightNow AI（月間 8356 訪問）、Not Diamond（月間 2.92万訪問）が含まれます。

TypeSafe AI is an AI lab building machine-native intelligence infrastructure, and Jev is its first System One Model — a model designed to make decisions inside software rather than reply to humans. Instead of returning strings, Jev accepts structured questions and returns typed decisions with calibrated confidence scores, so applications can act autonomously when confidence is high and escalate for human review when it is not. TypeSafe reports roughly 193x faster and 444x cheaper performance on System One workloads compared with general-purpose LLMs.

## 主なテレメトリと仕様

| 指標 | 値 |
| :--- | :--- |
| ツール名 | TypeSafe AI - Jev |
| ハンドル | typesafe-jev |
| Status | active |
| Origin | official |
| Maintenance | active |
| 公式ドメイン | typesafe.ai |
| ウェブサイト URL | https://typesafe.ai |
| カテゴリ | AIモデル, AI Workflow, AI Agent |
| 料金モデル | 有料 |
| 月間訪問数 | 6,148,688 |
| 前月比トラフィック変動 | -3,513,026 |
| 前月比成長率 | -36.4% |
| 主要市場 | アメリカ合衆国 |
| 市場シェア | 26.4% |
| ダイレクト訪問 | 3,141,114 |
| オーガニック検索訪問 | — |
| ソーシャル訪問 | — |
| リファラル訪問 | 472,731 |
| メール訪問 | 117,481 |
| レビュースコア | 4.2 |
| 保存数 | 0 |
| 対応プラットフォーム | ウェブサイト |
| 請求プラットフォーム |  |
| 属性 | 機能: ウェブサイト; 料金: 有料 |
| タグ | マシンネイティブAI, AI自動化, System Oneモデル, 型付き意思決定, キャリブレーションAI, 信頼度スコアリング, 意思決定の自動化, ソフトウェア連携, 構造化出力, ヒューマン・イン・ザ・ループ AI, 信頼できるAI, 低遅延AI, コスト効率の高い AI, キャリブレーション意思決定のための強化学習, RLCD, AIインフラストラクチャ, ワークフロー自動化, 自律型ソフトウェア, 本番運用 AI, 意思決定インテリジェンス, AI API, ハルシネーション低減, 機械学習, 開発者ツール |

## 概要

TypeSafe AI is an AI lab building machine-native intelligence infrastructure, and Jev is its first System One Model — a model designed to make decisions inside software rather than reply to humans. Instead of returning strings, Jev accepts structured questions and returns typed decisions with calibrated confidence scores, so applications can act autonomously when confidence is high and escalate for human review when it is not. TypeSafe reports roughly 193x faster and 444x cheaper performance on System One workloads compared with general-purpose LLMs.

## 料金とプラン

Paid, with early access to Jev. TypeSafe quotes Jev at $42 per billion input tokens and describes this as roughly 238x lower input pricing than comparable frontier chat models. Enterprise options and API access are arranged through the company, alongside documentation and an API console.

## 主な機能と特長

- **Typed decisions, not strings:** Jev outputs typed decisions that application code can act on directly, rather than prose that must be parsed and interpreted.
- **Calibrated confidence on every call:** Every decision carries an estimate of how confident the model is, so you can set thresholds for autonomous action versus human escalation.
- **Reported 193x faster, 444x cheaper:** TypeSafe reports Jev at roughly 193x faster and 444x cheaper than LLM-based workflows on comparable System One tasks, with a price of $42 per billion input tokens.
- **Built to reduce hallucination:** Because confidence estimates expose uncertainty, Jev is designed to avoid hallucination-style failures in automation, enabling zero-hallucination decision paths where confidence is high.
- **Composable workflows with human-in-the-loop:** Combine Jev's per-call decisions inside your own code to construct larger workflows, with explicit control over when intelligence acts and when it defers to a human.

## 主なユースケース

- **Build reliable automation workflows (Engineering teams automating decisions):** Replace text-generating model calls with typed decisions and confidence scores so pipelines can auto-approve clear cases and route ambiguous ones to a human, as in the reported 88% screening cost reduction case study.
- **Add decision intelligence to an app (Developers adding AI to products):** Classify and triage incoming items with low latency and low cost, escalating only the cases where confidence is below your threshold.
- **Run high-throughput classification (AI and data platform teams):** Insert a high-speed, low-cost model into automated data and content pipelines where decisions must be consistent and confidence-graded.
- **Benchmark decision models (Researchers and ML engineers):** Evaluate Jev's typed, calibrated outputs against existing LLM calls to compare latency, cost and reliability before wider rollout.

## 強み

- Purpose-built for machine consumers, not human chat
- Calibrated confidence enables principled human-in-the-loop thresholds
- Reported order-of-magnitude gains in speed and cost for decision tasks
- Typed outputs remove the parsing and validation overhead of JSON-mode LLMs
- Decision-level composability lets you build larger workflows in code

## 制約・デメリット

- Early access — general availability and some product details are still being finalised
- It is not a chatbot; teams expecting conversational output will need to restructure their workflows
- Requires engineering effort to consume typed decisions and set confidence thresholds in your own code
- Documentation and public pricing details are still evolving

## よくある質問

### What are System One Models?
System One Models are a class of AI model built to make decisions inside software rather than converse with people. They return typed outputs and calibrated confidence instead of free-form text.

### What is Jev?
Jev is TypeSafe's first public System One Model, optimised for automation. You send it structured questions and it returns typed decisions with probabilities and confidence estimates that your software can act on.

### Is Jev just a smaller LLM with structured output?
No. Jev uses a new architecture, a new sampler and a new training algorithm called Reinforcement Learning for Calibrated Decisions (RLCD). It is not a smaller LLM with a JSON wrapper — it is designed around typed decisions and calibrated confidence from the ground up.

### How is this different from a standard LLM with JSON mode?
General-purpose LLMs return words for people. Jev returns decisions that software consumes directly, more like code: reliable, fast, self-consistent and type-safe, with a confidence estimate attached to every decision.

### How do I get started and what does Jev cost?
Jev is in early access, and TypeSafe says it costs $42 per billion input tokens, which it describes as roughly 238x lower input pricing than comparable frontier chat models. You can request access through the site's API console and explore the documentation.

### TypeSafe AI - Jev の月間トラフィックと成長トレンドはどうですか？
TypeSafe AI - Jev は現在、月間 614.87万の訪問があり、前月比のトラフィック変動は -351.3万（成長率 -36.4%）です。主要な流入市場はアメリカ合衆国（シェア 26.4%）で、月間 314.11万のダイレクト訪問と月間 — のオーガニック検索訪問を含みます。

### TypeSafe AI - Jev は無料ですか？料金モデルは何ですか？
TypeSafe AI - Jev は有料の料金モデルで運営されています。Paid, with early access to Jev. TypeSafe quotes Jev at $42 per billion input tokens and describes this as roughly 238x lower input pricing than comparable frontier chat models. Enterprise options and API access are arranged through the company, alongside documentation and an API console. 対応する請求・チェックアウトプラットフォームには未掲載が含まれます。

### 2026年における TypeSafe AI - Jev の最適な代替ツールはどれですか？
OpenAITools.app のカテゴリ別トラフィックと機能の適合性に基づくと、TypeSafe AI - Jev の代替ツールトップ5は次のとおりです: 1. Jev AI（月間 2.96万訪問）; 2. RightNow AI（月間 8356 訪問）; 3. Not Diamond（月間 2.92万訪問）; 4. Emergence AI（月間 5.47万訪問）; 5. Decisions（月間 9.1万訪問）。

### TypeSafe AI - Jev はどのプラットフォームと主要な AI 機能に対応していますか？
TypeSafe AI - Jev は AIモデル, AI Workflow, AI Agent に分類され、以下のプラットフォームに対応しています: ウェブサイト。中核機能のタグにはマシンネイティブAI, AI自動化, System Oneモデル, 型付き意思決定, キャリブレーションAI, 信頼度スコアリングが含まれます。

## 代替ツール

| ツール名 | 月間訪問数 | 料金モデル | 比較 |
| :--- | :--- | :--- | :--- |
| [Jev AI](https://openaitools.app/ja/tool/jev-ai) | 29,625 | 無料 | [比較](https://openaitools.app/ja/compare/jev-ai-vs-typesafe-jev) |
| [RightNow AI](https://openaitools.app/ja/tool/rightnow-ai) | 8,356 | フリーミアム、有料 | [比較](https://openaitools.app/ja/compare/rightnow-ai-vs-typesafe-jev) |
| [Not Diamond](https://openaitools.app/ja/tool/not-diamond) | 29,244 | フリーミアム、有料、料金は要問い合わせ | [比較](https://openaitools.app/ja/compare/not-diamond-vs-typesafe-jev) |
| [Emergence AI](https://openaitools.app/ja/tool/emergence-ai) | 54,683 | 料金は要問い合わせ | [比較](https://openaitools.app/ja/compare/emergence-ai-vs-typesafe-jev) |
| [Decisions](https://openaitools.app/ja/tool/decisions-com) | 91,011 | 料金は要問い合わせ | [比較](https://openaitools.app/ja/compare/decisions-com-vs-typesafe-jev) |
| [OpenRouter](https://openaitools.app/ja/tool/openrouter-ai) | 19,941,437 | 有料 | [比較](https://openaitools.app/ja/compare/openrouter-ai-vs-typesafe-jev) |
| [AI/ML API](https://openaitools.app/ja/tool/ai-ml-api) | 232,946 | Pay-as-you-go credits (free to start); enterprise custom pricing on request | [比較](https://openaitools.app/ja/compare/ai-ml-api-vs-typesafe-jev) |
| [Mastra](https://openaitools.app/ja/tool/mastra) | 318,954 | フリーミアム、料金は要問い合わせ | [比較](https://openaitools.app/ja/compare/mastra-vs-typesafe-jev) |
| [Agent-Native](https://openaitools.app/ja/tool/agent-native) | 52,065 | 無料 | [比較](https://openaitools.app/ja/compare/agent-native-vs-typesafe-jev) |
| [OpenAI プラットフォーム](https://openaitools.app/ja/tool/openai) | 234,678,639 | 有料 | [比較](https://openaitools.app/ja/compare/openai-vs-typesafe-jev) |
| [Google AI Studio](https://openaitools.app/ja/tool/google-ai-studio) | 112,620,020 | フリーミアム | [比較](https://openaitools.app/ja/compare/google-ai-studio-vs-typesafe-jev) |

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出典: https://openaitools.app/ja/tool/typesafe-jev