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Top 113 Engineering automation workflows

Say goodbye to manual efforts with our Engineering automation workflows. Save time, increase efficiency, and drive better results with flexible and scalable marketing solutions!

Newcomer essentials: learn by doing

Creating an API endpoint

Task: Create a simple API endpoint using the Webhook and Respond to Webhook nodes Why: You can prototype or replace a backend process with a single workflow Main use cases: Replace backend logic with a workflow

Jonathan

AI key concepts

AI agent chat

n8n Team

AI chat with any data source (using the n8n workflow tool)

David Roberts

Chat with OpenAI Assistant (by adding a memory)

David Roberts

Use an open-source LLM (via HuggingFace)

n8n Team

+10

Chat with PDF docs using AI (quoting sources)

David Roberts

AI agent that can scrape webpages

Eduard

Featured AI templates

Auto-label incoming Gmail messages with AI nodes

This workflow uses AI to analyze the content of every new message in Gmail and then assigns specific labels, according to the context of the email. Default configuration of the workflow includes 3 labels: „Partnership” - email about sponsored content or cooperation, „Inquiry” - email about products, services, „Notification” - email that doesn't require response. You can add or edit labels and descriptions according to your use case. 🎬 See this workflow in action in my YouTube video about automating Gmail. Gmail trigger performs polling every minute for new messages (you can change the trigger interval according to your needs). The email content is then downloaded and forwarded to an AI chain. 💡 The prompt in the AI chain node includes instructions for applying labels according to the email content - change label names and instructions to fit your use case. Next, the workflow retrieves all labels from the Gmail account and compares them with the label names returned from the AI chain. Label IDs are aggregated and applied to processed email messages. ⚠️ Label names in the Gmail account and workflow (prompt, JSON schema) must be the same. Set credentials for Gmail and OpenAI. Add labels to your Gmail account (e.g. „Partnership”, „Inquiry” and „Notification”). Change prompt in AI chain node (update list of label names and instructions). Change list of available labels in JSON schema in parser node. Optionally: change polling interval in Gmail trigger (by default interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

Oskar

Compose reply draft in Gmail with OpenAI Assistant

Oskar

ChatGPT Automatic Code Review in Gitlab MR

assert

Write a WordPress post with AI (starting from a few keywords)

Giulio

+11

Suggest meeting slots using AI

n8n Team

Popular AI integrations

Chat with a Google Sheet using AI

David Roberts

Chat with OpenAIs GPT via a simple Telegram Bot

Mike

+10

Chat with PDF docs using AI (quoting sources)

David Roberts

Slack chatbot powered by AI

n8n Team

Trending $user.role templates

Trending AI templates

Verify and enrich your new leads and contacts

Scrape recent news about a company before a call

Milorad Filipović

Verify emails & enrich new form leads and save them to HubSpot

Niklas Hatje

Enrich new leads in Pipedrive and send an alert to Slack for high-quality ones

Niklas Hatje

Automate your outreach and follow-ups

Send follow-ups using Gmail to Hubspot contacts

Mutasem

Reach out via Email to new form submissions that meet a certain criteria

Niklas Hatje

Automatically email great leads when they submit a form and record in HubSpot

Mutasem

Gmail campaign sender: Bulk-send emails and follow up automatically if no reply

David Roberts

Featured templates

+26

Scale Deal Flow with a Pitch Deck AI Vision, Chatbot and QDrant Vector Store

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.