Long-form guides on the craft of AI-assisted deep research — how to do systematic literature reviews, structure citations, and build branching knowledge trees. Written like an academic survey: cited, footnoted, with references.
The Learn hub is the methodology layer behind Innogath. It explains how AI deep research should gather evidence, separate verified claims from weak leads, preserve citations, and branch complex questions without losing parent context.
Use these guides when you need more than a product tour: academic researchers can start with the systematic literature review guide, strategy teams can start with AI competitive intelligence, and knowledge workers can start with branching knowledge trees.
Deep research now means agentic search product, methodology, and output shape. Conflating them produces unreliable AI research workflows.
Read pillar guide 02 · Deep researchRun a systematic literature review with AI without losing rigor: protocol-first search, human screening, extraction checks, and PRISMA-ready records.
Read cluster guide 03 · Knowledge managementA branching knowledge tree is decision infrastructure, not memory. Here is what Luhmann actually built, what evergreen notes are not, and when tree fails.
Read cluster guide 04 · WorkflowsA research workflow is decided by a small set of choices made between stages, not by the stages themselves. Here is how to design one that survives revision.
Read pillar guide 05 · Deep researchUse AI for competitive intelligence without shipping guesses: define the decision, collect ethical sources, freshness-check claims, and cite every signal.
Read cluster guide 06 · WorkflowsStrategy research is deadline-driven, not truth-driven. The workflow is judged on the brief that survives a partner challenge by Friday.
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