Sapling's AI Content Detector helps organizations identify text likely written by AI. It analyzes passages for statistical patterns, highlights risky sentences, and provides a confidence score. Teams can run checks in the browser, upload batches, or call an API to scan large volumes. Integrations and policy settings support editorial, academic, and enterprise workflows while keeping reviewers in control. Results include sentence level heatmaps, explanations, and thresholds that match internal policies. Administrators can tune sensitivity and export findings for audits and training.
The detector evaluates each passage and sentence for signals associated with AI writing. It produces a document score and a per sentence heatmap so reviewers can focus on the most suspicious regions. Explanations summarize why a segment was flagged, and thresholds can be tuned for strict, balanced, or lenient modes. Reviewers can override results with notes, which are saved for audits and future calibration. Results are designed to guide human judgment rather than act as a final verdict to avoid false positives.
Sapling supports large scale checks through CSV uploads and a JSON API. Teams can stream documents from content systems, LMS platforms, or editorial queues for continuous scanning. API responses include scores, sentence highlights, and reasons, with status endpoints for long running jobs. Logs capture request IDs and timestamps to help operations troubleshoot, measure throughput, and maintain service levels. Webhooks can notify downstream systems when a job completes so actions are consistent and automatic.
The detector is trained to spot distribution shifts common in outputs from popular AI systems. It monitors token usage patterns, repetition, and structure to estimate the likelihood of AI authorship. Signals are updated as new model families and prompts emerge, and calibration data helps keep precision and recall in balance. Documentation cautions that no detector is perfect and encourages human review for high impact decisions. Guidance explains typical failure cases and how to set thresholds that match your risk tolerance.
A browser extension overlays sentence level indicators directly in web editors, email, and LMS tools. This allows quick checks during drafting and review without switching contexts. Organizations can deploy admin configurations, enable single sign on, and restrict features to specific groups. Editors see inline suggestions and links to the full report when deeper analysis or exports are required. The same policies apply across the extension and API so results are consistent for every reviewer.
Reports capture the document score, highlighted sentences, reviewer notes, and decisions. Exports in CSV or PDF support academic integrity cases, editorial standards, or enterprise compliance. Policy screens let administrators choose sensitivity levels, default actions, and retention. Privacy options limit storage of raw content and allow redaction so teams can meet regional data rules. Evidence is preserved for appeals and internal QA while minimizing exposure of sensitive content.
Recommended for universities, publishers, SEO teams, and enterprises that need a consistent process to assess authorship. Use it to screen assignments, submissions, knowledge base articles, guest posts, and vendor deliverables. Batch uploads and an API support large volumes, while the extension helps editors review in context. Policy controls and exports help organizations document enforcement and educate contributors about acceptable use. Admin features let departments tune settings to local guidelines without fragmenting standards.
Determining whether text was written by a person or a model is difficult and manual review is inconsistent. Sapling centralizes detection with sentence level evidence, thresholds, and reports. Teams can apply clear rules, reduce time spent on repetitive checks, and create a defensible record for policy disputes. The goal is to support reviewers, flag risk early, and reduce over reliance on gut feel during busy cycles. This lowers false alarms, speeds triage, and directs attention to the passages most likely to need edits.
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