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WriteGoAI Writing Assistant
Writer Agent무결성 검토AI 감지기가격
WriteGoAI Writing Assistant

WriteGo는 팀, 교육자, 출판사, 작성자가 AI 생성 텍스트를 감지하고 독창성 위험을 검토하며 책임 있게 글을 개선하도록 돕습니다.

AI 무결성 플랫폼

제품

  • Writer Agent
  • 글쓰기 무결성
  • 에세이 라이터
  • ChatPDF
  • 문헌 리뷰 매트릭스
  • AI 감지기
  • AI 휴머나이저
  • 기능
  • ChatGPT 탐지기
  • 글쓰기 도구
  • 문법 검사기
  • 리라이터
  • 요약 도구
  • AI Scholar
  • Paraphrasing Tool
  • Translator
  • Citation Generator
  • Scholar Search
  • 요금

리소스

  • 블로그
  • 리소스
  • 자주 묻는 질문
  • 방법론
  • 연구
  • AI 탐지 작동 방식
  • AI 탐지기 정확도
  • AI 탐지기 오탐지
  • 용어집
  • 대안
  • 비교
  • 리뷰

솔루션

  • 솔루션
  • 학문적 진실성
  • 검증된 콘텐츠
  • 문서 유형
  • 연동
  • 고객 지원 센터
  • 회사 소개
  • 문의하기

법적 고지

  • Editorial Policy
  • 개인정보 처리방침
  • 서비스 약관

© 2026 WriteGo. 모든 권리 보유.

AI 콘텐츠 감지와 글쓰기 무결성.

    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