AI Detector By Custom Writings vs Advacheck

CustomWritings runs a free AI detector inside its HowToWrite hub where students can paste text or upload DOC, TXT, PDF, and PowerPoint files to see what share of a paper looks machine-written. The checker returns an overall percentage and color-codes passages as likely or highly likely AI output, then lets you download a PDF report. It also ships as a browser extension for checking page content without leaving the site you are on.

Most standalone detectors sell themselves as neutral scanners. This one is built by an essay writing company that has offered academic help for 20 years and added combined AI and plagiarism screening to its own order checks in 2023. That parent service context shows up in the framing: the tool targets students who want to self-check before turning work in, not enterprise teams auditing marketing copy. The trade-off is independence, but the upside is a detector sitting next to free plagiarism, citation, and writing guide tools on the same domain.

Students prepping assignments, teachers reviewing suspicious submissions, and writers who lean on grammar tools all have reason to run a pass here. The FAQ notes that heavy grammar assist and stiff, template-like phrasing can sometimes trip the model even on human drafts, so shorter pieces under roughly 275 words may return less reliable scores than a full essay page.

Advacheck is a plagiarism and AI content detection platform for universities, publishers, researchers, and businesses. It scans documents for copied text, paraphrased passages, and machine-generated segments from models like ChatGPT, Gemini, Llama, and Claude.

The product line spans cloud tools (AI Detector, Advacheck.Basic, Expert, and Edu) plus on-premise software for organizations with strict security needs. Reports highlight matched sources, AI-likely sections, and image similarities, with LMS integrations such as Moodle for classroom workflows.

Advacheck has been developing language models since 1997 and positions its detection engine around low false-positive rates (under 0.5%). It is used to protect academic integrity, verify research manuscripts, and help marketers confirm content authenticity before publication.