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WriteGoAI Writing Assistant
Writer Agent整合性レビューAI 検出料金プラン
WriteGoAI Writing Assistant

WriteGo は、チーム、教育者、出版社、ライターが AI 生成テキストを検出し、独自性リスクを確認し、責任を持って文章を改善するのを支援します。

AI 整合性プラットフォーム

製品

  • Writer Agent
  • ライティング整合性
  • エッセイライター
  • ChatPDF
  • 文献レビューマトリクス
  • AI 検出器
  • AI ヒューマナイザー
  • 機能
  • ChatGPT検出器
  • ライティングツール
  • 文法チェッカー
  • リライター
  • 要約ツール
  • AI Scholar
  • Paraphrasing Tool
  • Translator
  • Citation Generator
  • Scholar Search
  • 料金

リソース

  • ブログ
  • リソース
  • よくある質問
  • 手法
  • 研究
  • AI検出の仕組み
  • AI検出精度
  • AI検出の誤検出
  • 用語集
  • 代替ツール
  • 比較
  • レビュー

ソリューション

  • ソリューション
  • 学術整合性
  • コンテンツ検証
  • 文書タイプ
  • 連携
  • ヘルプセンター
  • 私たちについて
  • お問い合わせ

法的事項

  • Editorial Policy
  • プライバシーポリシー
  • 利用規約

© 2026 WriteGo。無断転載を禁じます。

AI コンテンツ検出とライティング整合性。

    Resources

    AI Detection API Resources

    Learn how teams can integrate WriteGo detection into document review, risk routing, audit records, and writing-integrity workflows.

    Open core guide

    Discover capabilities before integration

    Use GET /v1/capabilities to list the available platform capabilities and GET /v1/capabilities/content.detect/schema to inspect the current detector input contract. Treat the returned schema as the source of truth instead of hard-coding assumptions from a marketing page.

    Authenticate with scoped API keys

    Platform requests use a WriteGo API key in the X-API-Key header. A detector client needs platform:runs:write to start a run and platform:runs:read to read its status; create the narrowest key your workflow needs and keep it outside browser page content, source control, and client logs.

    Start an idempotent detector run

    Submit text to POST /v1/capabilities/content.detect/runs. Send an Idempotency-Key when a client may retry so the same logical submission is not unintentionally started twice. Store the returned run identifier with your own document reference rather than using submitted text as an identifier.

    Poll status and preserve run evidence

    Read GET /v1/runs/{run_id} until the run reaches a terminal status. Keep the run ID, status, timestamps, capability version, relevant output, and your reviewer decision as separate fields so an audit record distinguishes model evidence from the human outcome.

    Handle failures, cancellation, and retries

    Clients should handle non-success HTTP responses, structured error messages, timeouts, and terminal failed or canceled states. Use POST /v1/runs/{run_id}/cancel when work should stop, and apply bounded backoff instead of aggressive polling or automatic infinite retries.

    Route uncertain documents to humans

    API-based workflows should not end with a score. High-risk, low-confidence, short, translated, edited, or policy-sensitive documents should move into a human review queue with the source context, passage evidence, notes, retention rules, and an appeal or correction path.

    FAQ

    What should an AI detection API workflow include?

    A strong workflow includes document IDs, risk bands, confidence, reviewer routing, audit records, retention rules, and policy status for each reviewed submission.

    Should API results make automatic decisions?

    No. API results should prioritize and route review. High-risk, low-confidence, or sensitive documents should move to a human reviewer before final action.

    Which endpoints start and read an AI detection run?

    Start a detector run with POST /v1/capabilities/content.detect/runs and read it with GET /v1/runs/{run_id}. Check the public capability schema first because supported fields can evolve with the versioned platform contract.

    How should API clients retry a submission?

    Use a stable Idempotency-Key for retries of the same logical submission, apply bounded backoff, and store the returned run ID. Do not create a new key for every network retry because that can create duplicate runs.

    Continue reading

    AI detection API capabilityAPI data retention guideReview queue guideAudit export guide