Streamlined BRD Generation Using GPT Agents and Google Workspace

If you have ever spent days writing a Business Requirements Document, you know the pain. Gathering stakeholder inputs, reviewing uploaded files, structuring sections, and keeping everything consistent takes forever. Now imagine someone submits a form with their project details and supporting documents, and within minutes, they get a professionally formatted BRD in their inbox. No manual work. No copy-pasting. Just pure automation magic.

That is exactly what this n8n workflow does. It uses AI agents, vector embeddings, and intelligent document processing to generate comprehensive Business Requirements Documents automatically. Think of it as having a business analyst working 24/7 who never gets tired and never misses a detail.

How This Workflow Solves the BRD Problem

The traditional BRD creation process is broken. Business analysts spend hours reading through emails, documents, and meeting notes. They structure requirements, write functional specifications, and format everything into a cohesive document. Then comes the back-and-forth for revisions.

This n8n workflow flips the script entirely. It accepts project information through a web form, processes all supporting documents using AI, generates a complete BRD with two specialized agents, and delivers it as a polished PDF via email. The entire process runs on autopilot.

Here is what makes it special. Instead of one generic AI trying to do everything, this workflow uses two specialized agents. One focuses on high-level content like executive summaries and project overviews. The other digs deep into business requirements and functional specifications. They work in parallel, pulling context from your uploaded documents using Retrieval Augmented Generation. The result is a BRD that actually understands your project.

Prerequisites: What You Need to Get Started

Before building this workflow, you will need access to a few services. The good news is most of them offer free tiers that are more than enough to get started.

Required API Connections

• OpenAI API: Powers the AI agents and document embeddings. You can use n8n's free credits to test, or connect your own API key.
• Google Sheets API: Tracks all BRD requests and uploaded documents in a centralized spreadsheet.
• Google Drive API: Stores supporting documents and creates the final BRD as a Google Doc.
• Gmail API: Sends the completed BRD to requesters with a professional email template.

Key Components in This n8n Workflow

This workflow uses a powerful combination of n8n nodes that work together seamlessly:

• Form Trigger: Creates a web form for collecting project details and file uploads
• Google Sheets: Manages request tracking and document records
• Google Drive: Handles file storage and document creation
• Extract from File: Pulls text content from PDF documents
• Vector Store: Embeds documents for AI retrieval
• AI Agent (LangChain): Two specialized agents for BRD generation
• OpenAI Chat Model: Provides the language models (GPT-4o and GPT-4.1-mini)
• Code Nodes: Custom JavaScript for data transformation and merging
• Gmail: Delivers the final document via email

Building the Workflow: Step-by-Step Implementation

Step 1: Create the Request Form and Document Processing Pipeline

Start by setting up a Form Trigger node that collects all necessary project information. Configure it to capture Project ID, Project Title, Business Domain (as a dropdown), supporting documents in PDF format, notes, and the requester's email address. Make all fields required to ensure you always have complete information.

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Next, build the document processing pipeline. Add a Code node to handle multiple file uploads, since users might submit several PDFs. This node splits the binary data so each file gets processed individually. Connect this to a Google Drive node that uploads each document to a dedicated folder for archival purposes. Simultaneously, send the files to an Extract from File node that pulls out all the text content from the PDFs.

Step 2: Set Up Request Tracking with Google Sheets

Create a Google Sheet with two tabs: one called Requests and another called Document. The Requests sheet should have columns for Project ID, Project Title, Business Domain, Notes, Email, Status, and Submitted At. The Document sheet tracks each uploaded file with its Drive link and metadata.

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Add a Code node that transforms the form data into the right structure, then connect it to a Google Sheets node set to append mode. This creates a record every time someone submits the form. For the uploaded documents, use another Code node to create individual records for each file, mapping them to the project ID for easy reference later.

Step 3: Build the Vector Storage System for AI Retrieval

This is where the magic happens. Take the extracted text from your PDFs and feed it into a Vector Store node using the in-memory storage option. Configure an OpenAI Embeddings node to convert your documents into vector representations. Set the memory key to something like vector_store_key so both AI agents can access the same knowledge base.

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The vector store acts as a searchable knowledge base. When your AI agents need context about the project, they query this store using natural language and get back the most relevant sections from your uploaded documents. This is Retrieval Augmented Generation in action.

Step 4: Configure the Dual AI Agent System

Here is where you deploy the two specialized agents. Set up a Google Sheets Trigger that polls your Requests sheet every five minutes looking for new entries. Add a Filter node that only processes requests where the Status is not Completed. This prevents duplicate processing.

Create the first AI Agent node and name it General BRD Writer Agent. Connect an OpenAI Chat Model node using GPT-4o. Give it a detailed system prompt that explains it is a business analyst specializing in executive summaries, project overviews, non-functional requirements, and implementation considerations. Connect your Vector Store node configured as a retrieval tool so the agent can query the knowledge base.

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Build the second AI Agent node called Business Requirement Writer Agent. This one uses GPT-4.1-mini and focuses exclusively on detailed business requirements and functional specifications. Its system prompt should emphasize creating requirement IDs, acceptance criteria, use cases with actors and flows, and data validation rules. Connect the same vector store so both agents access identical context.

Both agents receive the same input: the project details from the Google Sheet row. They work in parallel, each generating their specialized sections of the BRD in markdown format.

Step 5: Merge Content and Generate the Final Document

Add a Merge node to combine the outputs from both AI agents. Follow it with a Code node that concatenates the markdown content into a single cohesive document. This merged output becomes your complete BRD.

Now comes document creation. Use an HTTP Request node to call the Google Drive API directly with a multipart request. This converts your markdown content into a Google Doc on the fly. The request body should specify the document name (using project ID and title), set the MIME type to Google Docs format, and include your markdown content. Make sure to specify the parent folder ID so all BRDs end up in the same location.

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Once the Google Doc is created, add a Google Drive node to download it as a PDF. Configure the node to use Google's file conversion option, which automatically converts Docs to PDF format. Store this PDF for archival and email attachment.

Step 6: Deliver via Email and Update Tracking

Build a professional email delivery system using the Gmail node. Create an HTML email template that includes the project summary, a link to view the Google Doc, and the PDF as an attachment. The template should be clean and mobile-responsive, with key details displayed in a table format for easy scanning.

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Finally, close the loop by updating the Google Sheet. Add another Code node that sets the Status field to Completed, then connect it to a Google Sheets node in update mode. Use Project ID as the matching column to ensure you update the correct row. This prevents the workflow from processing the same request multiple times.

Benefits and Real-World Use Cases

The immediate benefit is obvious: what took hours or days now happens in minutes. But the real value goes deeper. This workflow creates consistency. Every BRD follows the same professional structure. Nothing gets missed because the AI agents are prompted to cover all standard sections. You get traceability because every request, document, and generated BRD is tracked in Google Sheets.

Software development teams can use this to kickstart projects faster. Product managers can request BRDs for feature initiatives without waiting for analyst availability. Consulting firms can scale their documentation services without hiring more writers. Even internal IT departments can standardize how they capture business requirements across different projects.

The workflow also handles multiple business domains through the dropdown field. Whether you are working in healthcare, finance, e-commerce, or manufacturing, the AI agents adapt their output based on the domain context pulled from your supporting documents.

Perhaps the most powerful aspect is the knowledge base approach. Because documents are embedded and queryable, the AI agents do not hallucinate or make up requirements. They ground their outputs in the actual materials you provided. This makes the generated BRDs trustworthy and ready for stakeholder review with minimal editing.

You can extend this workflow in countless ways. Add a Slack notification when a BRD is ready. Create a feedback loop where reviewers can request revisions through another form. Integrate with project management tools to automatically create Jira tickets from the functional requirements. The foundation is solid, and the possibilities are endless.

Building intelligent automation does not require a data science degree anymore. With n8n, a few API connections, and some clever prompting, you can create systems that genuinely feel magical. This BRD generator is proof that the future of documentation is not about writing faster. It is about not writing at all.