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How AI Tools Detector approaches privacy-first analysis, metadata-only reports, local research isolation and careful data minimisation.
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.
Internal 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.
Methodology detail
The product is designed to keep detection useful without turning submitted text into long-term report content. Reports focus on scores, signals, recommendations, IDs and timestamps.
Public methodology and transparency pages do not expose private research files, raw benchmark text, local datasets or internal benchmark samples.
Research outputs are designed around metrics and metadata such as hashes, counts, model names, score bands and false positive or false negative summaries. Raw benchmark text should remain local-only.
Benchmark datasets and RAID-style research workflows are kept separate from production routes, public hosting, Supabase storage and live detector behaviour.
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. Reports are designed to store privacy-safe metadata and guidance rather than raw submitted text.
No. Research datasets and raw benchmark text are not exposed through public pages or production routes.
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 details