Innogath vs Scholarly

Innogath is an AI research workspace that turns questions, files, links, and follow-up conversations into structured reports with citations, visual data blocks, and branching pages. Its Deep Research mode plans a task, reviews web pages or uploaded evidence, cross-checks claims, and produces a report with references. Reports can branch into child pages that retain summaries and citations from their parent. The workspace generates a visual knowledge map, and its canvas supports flowcharts, mind maps, timelines, charts, and comparison matrices. A rich-text Notebook supports project writing, and reports can be exported as PDF, Markdown, or DOCX. Supported uploads include PDFs, CSVs, documents, spreadsheets, and images; link inspection depends on the selected mode. A free plan includes 500 credits per month, basic-depth research, Markdown export, and one concurrent research task. Pro costs $115.20 a year, billed annually, and Ultra costs $32 a year, billed yearly. The service runs on macOS, Windows, and the web.

Scholarly is a browser-based AI workspace that uses your source material to create study and research outputs. Add PDFs, recordings, notes, slides, webpages, videos, images, YouTube links, or pasted text, then make flashcards, quizzes, practice exams, study guides, podcasts, video lectures, slides, mind maps, worksheets, and timelines. Chat answers include checked citations, with a Sources button opening the page supporting each claim. Deep Research searches the web, evaluates sources, and produces cited reports with charts and inline links.

Scholarly works in a browser on laptops, tablets, and phones, and syncs work through your account. Its Google Drive connection can use selected files as sources and save finished work as new Docs or Slides. Teams can use shared libraries, roles, central billing, and model controls. A free plan and free trial are available; paid plans start at $12/user/mo (annual). Scholarly says private user content is not used to train general-purpose AI by default; training requires separate opt-in.