AI Workflows
Artificial Intelligence tools and workflows
Automate YouTube Comment Sentiment Analysis and Reporting via Telegram
This workflow automates the retrieval and analysis of YouTube comments for sentiment and keywords, storing results in Google Sheets and sending a summary report to Telegram.
n8n$14.99Automate Image Editing with OpenAI ImageGen1 via n8n
Streamline your image editing process by integrating OpenAI ImageGen1 with n8n to automatically modify images based on prompts, enhancing efficiency for designers and marketers.
n8n$4.99Automate Hiring Briefs and Scorecards with AI and Google Docs
Streamline your recruitment process by transforming raw transcripts into polished hiring briefs and interview scorecards using AI and Google Docs in under a minute.
n8n$9.99Track and Estimate AI Model Token Usage and Costs with JinaAI
This n8n workflow helps AI engineers and automation specialists track token usage and estimate costs for AI models using JinaAI, logging data to Google Sheets for easy analysis.
n8n$9.99Automate LinkedIn Post Creation with AI and Human Review
This workflow automates the creation of research-based LinkedIn posts by integrating AI insights and human review. It generates a post draft, allows for human feedback, and creates a conceptual image, delivering the final content via email.
n8n$14.99Automate Sales Meeting Briefs with AI, LinkedIn, and WhatsApp Delivery
This n8n workflow automates the generation of sales meeting briefs by integrating Google Calendar, enriching attendee data, and using AI to summarize social media activity. The formatted summaries are delivered via email and WhatsApp, enhancing sales preparation.
n8n$14.99Batch Process Text Prompts with Anthropic Claude API
Efficiently send multiple text prompts to Anthropic's Claude models in a single batch request and retrieve results, optimizing processing time and scalability.
n8n$19.99Create a Dynamic AI Website Assistant with Pinecone and OpenAI
This workflow sets up an AI-driven virtual assistant that dynamically responds to website queries using Pinecone vector search and OpenAI models, with routing based on the source website.
n8n$19.99Interactive PDF Chatbot in Telegram Using Pinecone and OpenAI
This workflow enables a Telegram chatbot to interact with users by answering questions about the content of PDFs. It uses Pinecone for vector storage and OpenAI for generating embeddings, providing a seamless question-and-answer experience.
n8n$14.99Automate Online Tool Discovery and Evaluation with GPT-4o and SerpAPI
Streamline the process of finding and evaluating online tools with this workflow that leverages GPT-4o and SerpAPI to deliver comprehensive reviews and comparisons in seconds.
n8n$14.99Automate PDF to Blog Conversion with AI Agents
This workflow automates the transformation of complex PDFs into engaging blog posts using AI agents. It streamlines content creation by extracting key information and structuring it into a polished article ready for publishing.
n8n$14.99Generate a Legal Website Accessibility Statement with AI and WAVE
## Who is this for? This template is for any website owner, digital agency, or compliance officer operating within the **European Union**. It's designed for users who need to comply with the upcoming **European Accessibility Act (EAA)** but may not have deep technical or legal expertise. ## Disclaimer This workflow uses an npm package called cheerio to work with the specified URLs HTML code. Installing packages is only possible in self-hosting. ## What problem is this workflow solving? / Use Case Starting **June 28, 2025**, the European Accessibility Act (EAA) mandates that most websites offering products or services in the EU must be accessible and publish a formal Accessibility Statement. Manually creating this legal document is complex, requiring both a technical site analysis and knowledge of specific legal requirements. This workflow automates the generation of a compliant first draft, saving significant time and effort. ## What this workflow does After you input your details (like website URL and API key) in a central configuration node, this workflow automatically: 1. Scans your live website for accessibility issues using the powerful **WAVE API**. 2. Processes the scan results to identify the main problem areas. 3. Instructs a **Google Gemini AI agent** with a specialized legal prompt based on the European Accessibility Act. 4. Generates a formal Accessibility Statement in your desired language. 5. Saves the statement as an `.html` file and **sends it to you as an email attachment**. ## Setup This workflow is designed for a quick setup: 1. **Configure All Variables:** Click the **CHANGE THESE: dependencies** node. This is your central control panel. Fill in all the values, including your WAVE API Key, the URL to analyze, company details, and desired output language. 2. **Set Up Credentials:** You will need to connect your Google accounts for the workflow to run. * **Gemini:** Click the **gemini 2.5 pro** node, click the gear icon next to the Credential field, and connect your Google Gemini API credentials. * **Gmail:** Click the **Send report by email** node and connect your Gmail account to allow sending the final report. 3. **Activate & Execute:** Make sure the workflow is **active** in the top-right corner, then click **Execute Workflow** to run your first analysis. ## How to customize this workflow to your needs This template is a great starting point for any EU country. Here's how to adapt it: * **Localize for Your Country (Important!):** The generated statement contains a placeholder for the Enforcement Procedure. You **must** edit the prompt in the **Accessibility Statement Generator** node to replace this placeholder with the name and link to your specific country's official enforcement body. * **Change the AI:** Swap the Google Gemini node for any other AI model, like OpenAI or Anthropic Claude, by replacing the node and connecting it to the agent. * **Change the Trigger:** Replace the **When clicking 'Execute workflow'** node with a Form Trigger or Webhook Trigger to run this workflow based on external inputs, for example, to offer this analysis as a service to your clients.
n8n$9.99Automate Brand and Competitor Mentions Tracking with ChatGPT and Google Sheets
This workflow automates the daily monitoring of brand and competitor mentions in AI responses. It logs interactions in Google Sheets and sends a detailed email report, providing insights into AI-driven brand perception.
n8n$9.99Automate PDF to Blog Post Creation on Ghost
Transform PDF documents into engaging blog posts on Ghost using AI, saving time and enhancing content quality.
n8n$9.99Automate Blog Creation and Publishing with Gemini AI, Google Sheets, and GitHub
This n8n workflow automates the generation and publication of technical blog posts using topics from Google Sheets. It utilizes Gemini AI for content creation, commits the content to a GitHub repository, and updates a Jekyll-powered blog, streamlining the entire process.
n8n$14.99Interactive AI Chat with Google Search Console Data via OpenAI and Postgres
This workflow enables interactive communication with your Google Search Console data using an AI agent powered by OpenAI and Postgres. It facilitates natural language queries and retrieves data in a user-friendly chat interface.
n8n$14.99Message Buffer System with Redis for Efficient Processing
## Message-Batching Buffer Workflow (n8n) **This workflow implements a lightweight message-batching buffer using Redis for temporary storage and a JavaScript consolidation function to merge messages.** It collects incoming user messages per session, waits for a configurable inactivity window or batch size threshold, consolidates buffered messages via custom code, then clears the buffer and returns the combined response—all without external LLM calls. --- ### Key Features * **Redis-backed buffer** queues incoming messages per `context_id`. * **Centralized Config Parameters** node to adjust thresholds and timeouts in one place. * **Dynamic wait time** based on message length (configurable `minWords`, `waitLong`, `waitShort`). * **Batch trigger** fires on inactivity timeout or when `buffer_count` ≥ `batchThreshold`. * **Zero-cost consolidation** via built-in JavaScript Function (`consolidate buffer`)—no GPT-4 or external API required. --- ### Setup Instructions 1. **Extract Session & Message** * Trigger: `When chat message received` (webhook) or `When testing workflow` (manual). * Map inputs: set variables `context_id` and `message` into a Set node named **Mock input data** (for testing) or a proper mapping node in production. 2. **Config Parameters** * Add a Set node **Config Parameters** with: ``` minWords: 3 # Word threshold waitLong: 10 # Timeout (s) for long messages waitShort: 20 # Timeout (s) for short messages batchThreshold: 3 # Messages to trigger batch early ``` * All downstream nodes reference these JSON values dynamically. 3. **Determine Wait Time** * Node: **get wait seconds** (Code) * JS code: ```js const msg = $json.message || ""; const wordCount = msg.split(/\s+/).filter(w => w).length; const { minWords, waitLong, waitShort } = items[0].json; const waitSeconds = wordCount < minWords ? waitShort : waitLong; return [{ json: { context_id: $json.context_id, message: msg, waitSeconds } }]; ``` 4. **Buffer Message in Redis** * **Buffer messages**: `LPUSH buffer_in:{{$json.context_id}}` with payload `{text, timestamp}`. * **Set buffer_count increment**: `INCR buffer_count:{{$json.context_id}}` with TTL `{{$json.waitSeconds + 60}}`. * **Set last_seen**: record `last_seen:{{$json.context_id}}` timestamp with same TTL. 5. **Check & Set Waiting Flag** * **Get waiting_reply**: if null, **Set waiting_reply** to `true` with TTL `{{$json.waitSeconds}}`; else exit. 6. **Wait for Inactivity** * **WaitSeconds** (webhook): pauses for `{{$json.waitSeconds}}` seconds before batch evaluation. 7. **Check Batch Trigger** * **Get last_seen** and **Get buffer_count**. * IF `(now - last_seen) ≥ waitSeconds * 1000` OR `buffer_count ≥ batchThreshold`, proceed; else use **Wait** node to retry. 8. **Consolidate Buffer** * **consolidate buffer** (Code): ```js const j = items[0].json; const raw = Array.isArray(j.buffer) ? j.buffer : []; const buffer = raw.map(x => { try { return typeof x === 'string' ? JSON.parse(x) : x; } catch { return null; } }).filter(Boolean); buffer.sort((a, b) => new Date(a.timestamp) - new Date(b.timestamp)); const texts = buffer.map(e => e.text?.trim()).filter(Boolean); const unique = [...new Set(texts)]; const message = unique.join(" "); return [{ json: { context_id: j.context_id, message } }]; ``` 9. **Cleanup & Respond** * **Delete** Redis keys: `buffer_in`, `buffer_count`, `waiting_reply`, `last_seen` (for the `context_id`). * Return consolidated `message` to the user via your chat integration. --- ### Customization Guidance * **Adjust thresholds** by editing the **Config Parameters** node. * **Change concatenation** (e.g., line breaks) by modifying the `join` separator in the consolidation code. * **Add filters** (e.g., ignore empty or system messages) inside the consolidation Function. * **Monitor performance**: for very high volume, consider sharding Redis keys by date or user segments. --- © 2025 Innovatex Automation & AI Solutions [innovatexiot.carrd.co](https://innovatexiot.carrd.co/) [LinkedIn](https://www.linkedin.com/in/edisson-andres-garcia-herrera-63a91517b/)
n8n$14.99Transcribing Bank Statements to Markdown Using Gemini Vision AI
This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document, especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditional OCR because: - It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. - It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. - It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! ## How it works You can use the example bank statement created specifically for this workflow here: [https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing](https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing) - A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. - Because multimodal LLMs do not accept PDFs directly, we'll have to convert the PDF to a series of images. We can achieve this by using a tool such as [Stirling PDF](https://github.com/Stirling-Tools/Stirling-PDF/). Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. - Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. - Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. - Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a user message of type binary (data) which is how we add our image data as an input. - Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLM's ability to accurately read the page. - Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. ## Requirements - Google Gemini API for Multimodal LLM. - Google Drive access for document storage. - [Stirling PDF](https://github.com/Stirling-Tools/Stirling-PDF) instance for PDF to Image conversion ## Customizing the workflow - At the time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini, however, you can switch to other multimodal LLMs such as OpenAI GPT or Anthropic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. - Not parsing any bank statements any time soon? This template also works for invoices, inventory lists, contracts, legal documents, etc.
n8n$14.99Batch Upload Image Datasets to Qdrant for AI Analysis
This workflow automates the process of importing image datasets from Google Cloud Storage, creating embeddings using Voyage AI, and uploading them to Qdrant for anomaly detection and KNN classification.
n8n$14.99Automate Daily Online Presence Monitoring with AI Sentiment Analysis
This workflow provides a daily overview of your company's online presence by scanning multiple platforms, analyzing sentiment with AI, and delivering a structured report to your email.
n8n$14.99Automate Weekly Tech Research with AI, Notion, and Gmail
This workflow automates your weekly tech research and reporting using AI. It schedules tasks, gathers insights with AI, stores data in Notion, and sends summary reports via Gmail.
n8n$9.99Automate Image Generation with HeraNathalie Model via Replicate API
This workflow automates image creation using the Digitalhera Heranathalie AI model through the Replicate API, streamlining the process by handling API authentication, parameter setup, and result retrieval.
n8n$9.99Automate WordPress Category Mapping with Azure OpenAI's GPT-5 Mini
Streamline your WordPress content categorization by using Azure OpenAI's GPT-5 Mini to automatically map content topics to category IDs, reducing manual errors and speeding up publishing.
n8n$4.99Evaluate AI Response Relevance with OpenAI and Cosine Similarity
This workflow evaluates the relevance of AI agent responses by comparing generated questions with the original user questions using cosine similarity. It helps ensure AI responses are accurate and contextually appropriate.
n8n$14.99
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