Innogath
AI workspace for cited research and visual knowledge maps.
Innogath is an AI research workspace that helps users turn complex questions into structured research reports, cited sources, visual knowledge maps, branching pages, and saved notes.
Instead of keeping research inside a linear chat, Innogath lets users explore ideas non-linearly, follow up on specific branches, organize insights in a notebook, and export research-ready outputs.
Features
- AI-powered deep research reports
- Cited sources for better verification
- Visual knowledge maps
- Branching research pages
- Notebook for saving key insights
- Follow-up AI chat for deeper exploration
- Export-ready research outputs
Use Cases
- Research a complex topic from multiple sources
- Turn scattered information into a structured report
- Compare ideas, sources, or market opportunities
- Build a visual map of a research topic
- Save key findings and notes in one notebook
- Explore follow-up questions through branching pages
Comments
I built Innogath because most AI research still feels too linear. You ask a question, get an answer, then lose the structure as the topic becomes more complex. Innogath turns complex questions into cited research reports, visual knowledge maps, branching pages, and a notebook for saving important insights. The goal is to make deep research feel less like a long chat thread and more like a structured workspace for understanding complex topics.
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Comments
I built Innogath because most AI research still feels too linear. You ask a question, get an answer, then lose the structure as the topic becomes more complex. Innogath turns complex questions into cited research reports, visual knowledge maps, branching pages, and a notebook for saving important insights. The goal is to make deep research feel less like a long chat thread and more like a structured workspace for understanding complex topics.
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