Data & File Processing
Extracts named entities or keywords from unstructured text and presents them in tables.
A focused file analysis agent for analysts
Entity and Keyword Extractor is an AI agent built to extract named entities or keywords from unstructured text and present them in tables. It is built for analysts, operators, researchers, and teams working with files who need to avoid manually scanning documents for names, organizations, products, or recurring terms. Add text or document file, types of entities to extract, and output format; the agent turns those inputs into entity tables, frequency counts, unclear matches, and false-positive notes. Run it once per file batch or dataset, then reuse the slots whenever the input format repeats.
Start with text or document file. Then add types of entities to extract and output format so the agent has enough context to produce entity tables, frequency counts, unclear matches, and false-positive notes.
Yes. That is one of the core outputs. More specific inputs produce more specific results.
It asks for the details most likely to change the answer, especially text or document file, types of entities to extract, and output format. That prevents manually scanning documents for names, organizations, products, or recurring terms.
Yes. Use the file-or-text slots for spreadsheets, documents, transcripts, exports, or pasted text, then specify the exact extraction or analysis goal.
Yes. Add your preferred format, examples, tools, or constraints in the slots, and the agent can shape the result around them.
Clarify text or document file, add missing constraints, and state what a good result should include. The next run will usually improve when the failure mode is explicit.