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This agentic workflow generates a structured SOAP note from a medical transcript by extracting subjective and objective information, assessing the data, and formulating a treatment plan.

This agent summarizes a PDF document by processing its contents through the Vellum Document API and generating a user-friendly summary. It allows users to input a document ID and receive a concise summary of the document's text.

This agentic workflow extracts data from PDF files and converts it into structured CSV format. It processes each page of the PDF, generating separate CSV outputs for menu items, invoices, and product specifications.

This agent is an Q&A chatbot (RAG) that leverages a search engine and a reranking model to provide accurate answers based on internal policy documents. It processes user queries and retrieves relevant policy excerpts, ensuring responses are well-cited and contextually grounded.

This agent extracts and reviews financial statements and their corresponding footnotes from SEC 10-K filings. It identifies major financial statement tables and verifies the accuracy and completeness of footnotes, ensuring compliance with U.S. GAAP and SEC regulations.

This agent is an Q&A chatbot (RAG) that leverages a search engine and a reranking model to provide accurate answers based on internal policy documents. It processes user queries and retrieves relevant policy excerpts, ensuring responses are well-cited and contextually grounded.

This agent generates a synthetic dataset for testing an AI pipeline by creating test cases based on user-defined parameters. It allows users to specify the purpose of the AI pipeline, the number of test cases to generate, and any additional context, then outputs the test cases formatted as an API request body.

This AI pipeline creates a fully functional chatbot that uses a vector database. It enables you to upload internal documentation (like security policies) and instantly answer user questions grounded in those docs, complete with citations for transparency.