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Everything in InsightHub is organized around two building blocks: topics and documents.

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Topics

A topic represents a knowledge domain — a subject area you want to explore. It is the container for a set of related documents and the entry point for starting explorations. A well-scoped topic makes exploration answers more precise because the AI searches within a bounded, relevant set of documents. Rather than putting everything into one topic, create separate topics for distinct domains:
  • “Product specs — v2.3”
  • “Q4 investor materials”
  • “Customer onboarding guides”

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Documents

Documents are the source material inside a topic. When you upload a file, InsightHub processes it into a form the AI can work with: 
Upload
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Text extraction
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Chunking (split into passages)
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Embedding (passages converted to vectors)
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Indexed in the vector store
When the AI answers a question during an exploration, it searches these indexed passages — not the raw files. The answer is assembled from retrieved chunks, not recalled from general training knowledge.

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How they shape exploration answers

The AI during an exploration only has access to documents within the current topic. This means:
  • Answers are scoped — the AI answers from your documents, not general knowledge alone
  • Boundaries matter — if a relevant document is not in the topic, the AI cannot reference it
  • Document quality counts — clean, on-topic files improve retrieval accuracy
If an exploration answer seems incomplete or off-target, check that the relevant documents are uploaded, fully processed (Ready status), and belong to the correct topic.

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Next steps

RAG

Understand how the AI retrieves and uses document passages to answer questions.