2 posts tagged with "semantic search"

"I spend too much time hunting through PDFs" is usually not a PDF problem. It is a workspace problem. The answer might be in a PDF appendix, a Word draft, a spreadsheet row, a Markdown note, or an older file someone forgot to rename.

The practical way to search across hundreds of PDFs, Word documents, and Excel files with AI is to index the folder once, use both keyword and semantic search, open the original sources behind each result, and treat the AI answer as a reviewable draft rather than a final authority.

Document.Bot is built for that workflow: point it at a folder, search mixed formats, ask source-backed questions, and inspect the documents behind the answer.

Keyword search and semantic search solve different problems in document folders. Keyword search is best when the exact text matters. Semantic search is best when the meaning matters but the wording varies.

For serious document work, the best answer is usually not one or the other. A high-recall workflow uses both, keeps the original source open for review, and treats AI output as a draft.

Document.Bot is built around that hybrid search loop for folders of PDFs, Word files, spreadsheets, Markdown, and notes.