Back to Catalog

Sync Attio CRM with Jotform & Slack for deal updates & sales alerts

AppUnits AIAppUnits AI
39 views
2/3/2026
Official Page

This workflow streamlines your lead management process by automatically capturing form submissions from Jotform, updating Attio CRM, and notifying your team (sales team for example) via Slack — all without manual work.

How it works

  1. Receive Lead:
  • A new submission is captured from Jotform (name, email, message).
  1. Prepare CRM:
  • Checks if the Pending and Urgent deal stages exist in Attio CRM and creates them if they don’t exist.
  • Checks if the Message column exists in Attio CRM and creates it if it doesn't exist.
  1. Lead Handling:
  • If the lead doesn't exist in Attio CRM, the contact is created, a new deal is added to the Pending stage, and a Slack notification is sent.
  • If the lead exists but has no deal, a new deal is added to Pending, and Slack is notified.
  • If the lead exists with a deal, the deal is moved to the Urgent stage, and Slack is notified.
  1. Slack Notification:
  • Your team (sales team for example) receives an instant Slack message whenever a new or existing lead is processed, so they can act fast.

Requirements

Make sure to have Jotform, Attio CRM and Slack accounts, then follow this video guide on how to start using this template.

n8n Workflow: Basic Workflow Structure Example

This n8n workflow demonstrates a fundamental structure for processing data, including receiving input, applying conditional logic, transforming data, and making an HTTP request. It serves as a template for building more complex automation flows.

Description

This workflow outlines a basic data processing pipeline within n8n. It starts by accepting incoming data via a webhook, then applies conditional logic to route the data, performs data manipulation, and finally makes an external HTTP request. This structure is useful for understanding how to connect core n8n nodes to create a functional automation.

What it does

  1. Receives Data via Webhook: The workflow is triggered by an incoming HTTP request to a defined webhook URL.
  2. Applies Conditional Logic (If): It evaluates the incoming data against a set of conditions. Data that meets the conditions will proceed through the "true" branch, while data that does not will proceed through the "false" branch (though the false branch is not explicitly connected in this example).
  3. Transforms Data (Edit Fields): On the "true" branch of the If node, the data is passed to an "Edit Fields (Set)" node, which allows for adding, modifying, or removing fields from the incoming JSON data.
  4. Further Conditional Logic (Switch): The workflow then uses a Switch node to route data based on specific values within a field. This allows for multiple distinct paths depending on the data content.
  5. Executes Custom Code: One path from the Switch node leads to a Code node, enabling the execution of custom JavaScript logic to further process or transform the data.
  6. Makes an HTTP Request: Finally, an HTTP Request node is used to send data to an external API or service.
  7. Provides a Data Table: A Data table node is included, which can be used for displaying or temporarily storing structured data within the workflow for debugging or reference.
  8. Includes a Sticky Note: A Sticky Note node is present, likely for documentation or temporary notes within the workflow canvas.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n (either cloud or self-hosted).
  • Webhook Trigger: An external system capable of sending HTTP requests to the n8n webhook URL.
  • External API/Service: If the HTTP Request node is configured, an external API or service to send data to.

Setup/Usage

  1. Import the Workflow:
    • Copy the provided JSON code.
    • In your n8n instance, click "New" in the workflows section.
    • Click the "Import from JSON" button and paste the copied JSON.
  2. Activate the Webhook:
    • Locate the "Webhook" node (ID: 47).
    • Click on it to open its settings.
    • Copy the "Webhook URL" and configure your external system (e.g., Jotform, another application) to send POST requests to this URL.
    • Ensure the workflow is "Active" (toggle in the top right corner).
  3. Configure Nodes (as needed):
    • If Node (ID: 20): Adjust the conditions to match your specific data filtering requirements.
    • Edit Fields (Set) Node (ID: 38): Modify the fields to be added, updated, or removed based on your data transformation needs.
    • Switch Node (ID: 112): Define the expressions and cases for routing data to different paths.
    • Code Node (ID: 834): Write your custom JavaScript logic to process the data as required.
    • HTTP Request Node (ID: 19): Configure the URL, method, headers, and body for the external API call.
    • Data table (ID: 1315): Populate this with relevant data for testing or reference.
  4. Test the Workflow: Send a test request to the Webhook URL to observe the data flow and ensure all nodes are functioning as expected.

Related Templates

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

Automate RSS to social media pipeline with AI, Airtable & GetLate for multiple platforms

Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (to-share-linkedin, to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags social-media-automation, ai-content-generation, rss-to-social, multi-platform-posting, getlate-api, airtable-database, langchain, workflow-automation, content-marketing

Mikal Hayden-GatesBy Mikal Hayden-Gates
188