Deep Review

Compare multiple sources across themes in a single table.

Paste one source per line: title, authors, abstract or notes, and build a matrix that aligns each source against recurring themes. Instead of re-reading everything, you see agreements, gaps and contradictions at a glance, ready to turn into a literature review. The structured output is immediately usable as the skeleton of a literature review section, saving hours of manual organisation.

Check whether a source actually supports, or contradicts, a claim.

Before you cite, confirm the source says what you think it says. Claim checking surfaces whether the evidence supports or contrasts your statement, so you avoid mis-citation and build arguments on solid ground. This extra check is especially valuable in fast-moving fields where a source's conclusion is often more nuanced than its title suggests.

Confirm your writing is original before you submit.

Run similarity checks to catch overlapping text and unintentional copying while there is still time to paraphrase or cite. Detailed feedback shows exactly where attention is needed, protecting your academic integrity. Catching potential issues before submission, rather than after, gives you the time to address them properly without last-minute pressure.

Verify how much of a passage reads as AI-generated.

Check drafts for AI-generated phrasing so your submitted work reflects your own voice and meets your institution's expectations on responsible AI use, part of a transparent, integrity-first workflow. Proactively checking your own drafts demonstrates due diligence and builds a clear record of responsible AI use you can show reviewers or supervisors.