Start with templates

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 transforms a LinkedIn post into a structured article and creates a new page in Notion with the generated content.

This workflow processes legal documents to generate a comprehensive legal research memorandum, ensuring citation accuracy and relevance. It enhances user queries, searches for relevant documents, analyzes findings, validates citations, and synthesizes results into a structured output.

This AI agent workflow automates the assessment of legal contracts by parsing them, checking for required clauses, assessing risks, and generating a summary report. It helps legal teams quickly identify potential issues in contracts and provides actionable insights.

This workflow creates a legal RAG chatbot that generates comprehensive legal answers based on user queries and relevant legal documents. It allows users to search legal documents, format the results, and provide answers with proper citations in both markdown and JSON formats.

This workflow generates a comprehensive legal research memo and a reading list based on a specified research question, jurisdiction, and date range. It validates citations and retrieves relevant legal documents to support the research.

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 workflow creates a e-commerce support agent that assists customers with various e-commerce tasks, such as checking order status, managing shopping carts, and processing returns. It leverages AI to provide real-time responses based on user queries and actions.

This workflow creates a retail pricing optimization tool that analyzes product data and market conditions to recommend optimal pricing strategies. It helps businesses set competitive prices while ensuring profitability and compliance with business rules.

This workflow generates a comprehensive risk assessment for suppliers based on various data inputs. It evaluates financial, performance, and news-related factors to provide a risk score, detailed assessment, and specific recommendations for risk mitigation.

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 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.

This workflow generates a comprehensive strategy based on research from LinkedIn and company data. It helps sales teams prepare for meetings by providing tailored insights and talking points.

This workflow extracts case study information from a specified URL and organizes it into a Notion page. It automates the process of finding relevant case studies, scraping their content, and formatting the data for easy access and readability.

This agentic workflow generates a 30 day LinkedIn content plan based on your content goals, target audience, and business information. It automates the process of generating content ideas, drafting posts, and organizing them into Notion for easy access and management.

This ReAct agent can perform web searches and scrape web pages based on user queries. It allows users to interact with the agent, which gathers information from the internet and provides responses with citations.

This agentic workflow generates a 30 day LinkedIn content plan based on your content goals, target audience, and business information. It automates the process of generating content ideas, drafting posts, and organizing them into Notion for easy access and management.

This agent transforms a LinkedIn post into a structured article and creates a new page in Notion with the generated content.

This workflow creates a legal RAG chatbot that generates comprehensive legal answers based on user queries and relevant legal documents. It allows users to search legal documents, format the results, and provide answers with proper citations in both markdown and JSON formats.

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 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.

This workflow generates a comprehensive risk assessment for suppliers based on various data inputs. It evaluates financial, performance, and news-related factors to provide a risk score, detailed assessment, and specific recommendations for risk mitigation.

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 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 workflow creates a legal RAG chatbot that generates comprehensive legal answers based on user queries and relevant legal documents. It allows users to search legal documents, format the results, and provide answers with proper citations in both markdown and JSON formats.

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 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.