Back to Catalog

Create professional proposals using Dual AI and Google Docs templates

KeanKean
104 views
2/3/2026
Official Page

How it works

  1. Input your proposal basics - Manually enter the core details and key points for your proposal
  2. Dual AI processing - OpenAI expands your inputs into a comprehensive draft, then Claude refines it for clarity and readability
  3. Automated document output - The workflow copies your Google Doc template, replaces all variables with the AI-generated content, and delivers your finished proposal

Set up steps

Estimated time: 10-15 minutes

  1. Create an OpenRouter account - Sign up at OpenRouter to get API access for Claude
  2. Set up your Google Doc template - Create a template document with placeholder variables (variable names are listed in the 'Update proposal' node)
  3. Configure API credentials - Add your OpenAI and OpenRouter API keys to the workflow
  4. Connect Google Drive - Authenticate your Google account to enable document creation

💡 Detailed configuration instructions and variable naming conventions can be found in the sticky notes within the workflow.

Create Professional Proposals Using Dual AI and Google Docs Templates

This n8n workflow automates the creation of professional proposals by leveraging the power of two AI models (OpenAI and OpenRouter) and Google Docs templates. It allows you to generate dynamic content for your proposals and then populate a Google Docs template with the AI-generated text.

What it does

  1. Manual Trigger: Initiates the workflow when you click 'Execute workflow' in n8n.
  2. Edit Fields (Set): This node is a placeholder, likely intended for defining or modifying input data for the proposal generation. It currently doesn't have specific data defined in the provided JSON but can be configured to set variables like client name, project details, or specific proposal sections.
  3. Basic LLM Chain (OpenAI): Utilizes an OpenAI Large Language Model (LLM) to generate a portion of the proposal content. This could be for an executive summary, project scope, or other sections requiring creative text generation.
  4. Structured Output Parser: Processes the output from the OpenAI LLM, likely to extract specific data points or format the generated text into a structured format (e.g., JSON) that can be easily used in subsequent steps.
  5. OpenRouter Chat Model: Employs an OpenRouter Chat Model, potentially for generating alternative sections of the proposal, refining existing content, or providing a different perspective, showcasing a "dual AI" approach.
  6. Google Docs: Interacts with Google Docs, likely to open a predefined template document.
  7. Google Drive: This node is also a placeholder, potentially for saving the generated document or retrieving templates from Google Drive.
  8. Sticky Note: A sticky note is present, likely for documentation or comments within the workflow itself, indicating areas for improvement or specific instructions.

Prerequisites/Requirements

  • n8n Account: An active n8n instance (cloud or self-hosted).
  • OpenAI API Key: For the "Basic LLM Chain" node to access OpenAI's language models.
  • OpenRouter API Key: For the "OpenRouter Chat Model" node to access OpenRouter's language models.
  • Google Account: With access to Google Docs and Google Drive.
  • Google Docs Template: A pre-designed Google Docs template with placeholders for the AI-generated content.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API Key credential for the "OpenAI" node.
    • Set up your OpenRouter API Key credential for the "OpenRouter Chat Model" node.
    • Configure your Google OAuth2 credential for the "Google Docs" and "Google Drive" nodes.
  3. Customize "Edit Fields" Node:
    • Modify the "Edit Fields" (Set) node to define the input data required for your proposals (e.g., client name, project description, specific requirements). This data will be passed to the AI models.
  4. Configure AI Nodes:
    • Adjust the prompts and model parameters within the "Basic LLM Chain" (OpenAI) and "OpenRouter Chat Model" nodes to generate the desired proposal content.
    • Ensure the "Structured Output Parser" is configured to correctly parse the AI output for your Google Docs template.
  5. Configure Google Docs Node:
    • Specify the ID of your Google Docs template document.
    • Map the AI-generated content to the placeholders within your Google Docs template.
  6. Execute Workflow: Click "Execute workflow" on the "Manual Trigger" node to run the automation. The workflow will generate the proposal content and populate your Google Docs template.

Related Templates

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate Dutch Public Procurement Data Collection with TenderNed

TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte

Wessel BulteBy Wessel Bulte
247

🎓 How to transform unstructured email data into structured format with AI agent

This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.

DavideBy Davide
1616