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How AI Tools Detector approaches probabilistic AI writing analysis, provider-assisted scoring and responsible interpretation.
Core principle
AI detection results are guidance only.
No detector can prove authorship with certainty. These pages explain how to interpret results without overstating what automated analysis can prove.
Trust framework
The detector reviews writing behaviour such as repetition, structure, phrasing consistency, sentence variety and context-specific signals. It explains what influenced a result instead of presenting a black-box accusation.
Human and AI writing can overlap, especially in academic, formal, translated, edited or templated text. False positives and false negatives are possible, so results should guide review rather than decide outcomes alone.
Reports are designed around metadata, scores, signals, timestamps and guidance. Raw submitted text, uploaded file contents, prompts and provider payloads are not stored in reports.
Use detector outputs to support human review, editorial improvement, policy conversations and proportionate follow-up. Avoid punishment-only decisions based on an automated result.
Methodology detail
AI detection works with signals, probabilities and context. It can highlight patterns that often appear in AI-assisted writing, but it cannot prove who wrote a text or what tools were used.
The detector focuses on writing signals such as structure, repetition, phrasing consistency, generic transitions, low personal detail and unusually polished patterns. These signals are presented as context for review, not as an accusation.
AI Tools Detector keeps analysis services and credentials server-side. Results are normalised into a consistent report experience without exposing service internals.
A result can support a conversation or review workflow, but it should not be used on its own to accuse someone of misconduct. Mixed writing, editing tools, translation and templated content can all affect the result.
Methodology pages explain the review approach, known limitations and why results should not be treated as proof.
Available resourceReports organise confidence, writing signals, recommendations and limitation notes into a review-friendly format.
Available resourceThe public status and transparency pages explain service availability, research principles and responsible claims.
Available resourcePrivacy pages explain temporary processing, report ownership and the platform's data-minimisation approach.
Available resourceFAQ
Short answers for responsible interpretation and privacy-first use.
No. AI detection results are guidance only. No detector can prove authorship with certainty.
Probabilities communicate uncertainty better than yes-or-no labels. They help reviewers understand risk without treating the result as absolute proof.
Content discovery
Move from methodology into practical guides, privacy pages and product workflows.
Transparency
Explore the transparency pages that explain methodology, privacy, limitations and responsible use.
AI detection results are guidance only. No detector can prove authorship with certainty, and important decisions should include human review and appropriate context.
Scan reports are designed to show scores, signals and guidance without storing the original submitted text.
Read privacy detailsInternal research and benchmark work is used to improve calibration and understand failure modes. Public pages avoid unverifiable accuracy claims and do not expose private datasets or raw benchmark text.