Use case
After agents write prose in bulk, a content or engineering team needs to know which passages carry AI-writing patterns, so sloptrim scans each text file locally and returns a score to decide what needs human rewriting.
Today teams skim by hand, judge by experience, or use online AI-detection services that require uploading text and give unstable results.
Agent output is voluminous and uniform in tone; reading every piece by hand is costly, and discovering the machine-written feel after publishing forces expensive rework.
xOcto's call
Demand is evidenced
The trend: agents now emit text in bulk, so nobody reads every sentence and quality control moves forward to the moment of saving. Entry point: steps where output is published and being read as machine-written is a risk, such as brand copy, bid documents or academic pre-screening, priced per file or seat, though no price is disclosed.
Reason to use it
Why users would choose it
Inference: versus uploading to an online detector or skimming by hand, it scores each saved file locally and instantly, removing the upload-and-wait step, so teams worried about outbound copy would use it at save time; no retention or repeat-use evidence yet.
Where the easy answer breaks down
The tension worth following
An English validation note will follow from the public evidence.
If this is your job
Worth trying. Inference: versus uploading to an online detector or skimming by hand, it scores each saved file locally and instantly, removing the upload-and-wait step, so teams worried about outbound copy would use it at save time; no retention or repeat-use evidence yet.
Entry and what to borrow
The trend: agents now emit text in bulk, so nobody reads every sentence and quality control moves forward to the moment of saving. Entry point: steps where output is published and being read as machine-written is a risk, such as brand copy, bid documents or academic pre-screening, priced per file or seat, though no price is disclosed.