ExtractFromFile Automate

ExtractFromFile Automate streamlines the RFP response process by automatically extracting questions from submitted documents, generating tailored responses using AI, and organizing everything in a dedicated Google Doc. It enhances efficiency by notifying the team via email and Slack once the task is complete, allowing for quicker turnaround times on RFPs and improved collaboration.

7/8/2025
23 nodes
Complex
webhookcomplexextractfromfilelangchainsplitinbatchessticky notegoogledocsslackgmailadvancedfilesstoragecommunicationnotificationintegrationapi
Categories:
Communication & MessagingComplex WorkflowWebhook Triggered
Integrations:
ExtractFromFileLangChainSplitInBatchesSticky NoteGoogleDocsSlackGmail

Target Audience

This workflow is designed for:
- Sales Teams: Looking to streamline the response process for RFPs, allowing for quicker and more efficient replies.
- Project Managers: Who need to manage multiple RFPs simultaneously and ensure all responses are documented accurately.
- Business Development Professionals: Aiming to enhance their proposal quality using AI-generated insights and answers.
- Technical Teams: Responsible for implementing and maintaining automated workflows to improve operational efficiency.

Problem Solved

This workflow addresses the challenge of manually extracting questions from RFP documents and generating responses efficiently. By automating the process, it reduces the time spent on document handling, enhances accuracy in responses, and ensures that no critical questions are overlooked. The integration of AI allows for contextual answers, improving the quality of submissions and increasing the chances of winning bids.

Workflow Steps

  • Receive RFP Document: The workflow starts by receiving an RFP document via a webhook. Users must submit the document through an API request.
    2. Extract Data: The document is processed to extract relevant data using the ExtractFromFile node, focusing on PDF files.
    3. Set Variables: Key variables such as document title, filename, and the reply-to address are set for later use.
    4. Create RFP Response Document: A new Google Docs document is created to store the responses, ensuring each RFP has a dedicated space for answers.
    5. Extract Questions: AI is employed to identify and extract questions from the RFP, regardless of formatting, using the LangChain capabilities.
    6. Generate Answers: For each extracted question, the workflow loops through and generates answers using an OpenAI Assistant, ensuring contextually relevant responses.
    7. Record Answers: Each question and its corresponding answer are recorded in the Google Docs document.
    8. Add Metadata: Metadata including the document title, generation date, and requester details are added to the document for clarity and tracking.
    9. Send Notifications: Upon completion, notifications are sent via email and Slack to inform the team and the requesting user that the RFP response is ready.

    • Customization Guide

    To customize this workflow, users can:
    - Modify Webhook Settings: Adjust the webhook path and HTTP method as needed to fit their API requirements.
    - Change Google Docs Folder: Update the folder ID where new documents will be created to organize responses according to their structure.
    - Customize Questions Extraction: Modify the AI prompt used for extracting questions to better suit specific RFP formats or requirements.
    - Adjust Notification Settings: Update the Slack channel and email addresses in the notification nodes to ensure the right team members are informed.
    - Enhance AI Responses: Tweak the OpenAI Assistant settings and uploaded documents to improve the quality of answers based on the organization's specific context and needs.