[ KNOWLEDGE_WORK ]
Document & Notes Workspace with Semantic Search
What it is
A team workspace where documents and notes are both searchable by meaning.
[ THE_PROBLEM ]
Why this existed
Research teams keep source documents in one place and their own analysis in another, and can search neither by meaning. The insight written six months ago is effectively lost.
[ WHAT_WE_BUILT ]
What we built
Document upload with per-document chat, a notes system, and a query surface that searches across both by semantic similarity rather than keyword. Organisation memberships control who sees what.
- Document upload with per-document chat
- Notes system alongside stored documents
- Semantic search across both documents and notes in one query
- Organisation memberships controlling access
[ HOW_IT_IS_USED ]
How a company uses it
A research or advisory team keeps its source documents and its own written analysis in one place and can ask a question across both at once.
Built with
[ COMMON_QUESTIONS ]
Questions clients ask
Why search notes and documents together?
Because the answer to a research question is often half in a source and half in what a colleague already concluded about it. Searching them separately means finding one and missing the other.
How does semantic search differ from keyword search?
Keyword search requires you to guess the words the author used. Semantic search matches on meaning, so a note about "counterparty exposure" surfaces for a query about "credit risk". For research archives written by different people over years, that difference is substantial.
Can this scale to a large document set?
The architecture does, though at large volumes the interesting problems shift to chunking strategy, retrieval quality and cost per query rather than storage. Those are tuning questions we would want to work through against your actual corpus.
[ RELATED_WORK ]
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