Parse and extract invoice data with Nanonets OCR and export to Excel
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automates document processing and structured table extraction using the Nanonets API. You can submit a PDF file via an n8n form trigger or webhook—the workflow then forwards the document to Nanonets, waits for asynchronous parsing to finish, retrieves the results (including header fields and line items/tables), and returns the output as an Excel file. Ideal for automating invoice, receipt, or order data extraction with downstream business use. How It Works A document is uploaded (via n8n form or webhook). The PDF is sent to the Nanonets Workflow API for parsing. The workflow waits until processing is complete. Parsed results are fetched. Both top-level fields and any table rows/line items are extracted and restructured. Data is exported to Excel format and delivered to the requester. Setup Steps Nanonets Account: Register for a Nanonets account and set up a workflow for your specific document type (e.g., invoice, receipt). Credentials in n8n: Add HTTP Basic Auth credentials in n8n for the Nanonets API (never store credentials directly in node parameters). Webhook/Form Configuration: Option 1: Configure and enable the included n8n Form Trigger node for document uploads. Option 2: Use the included Webhook node to accept external POSTs with a PDF file. Adjust Workflow: Update any HTTP nodes to use your credential profile. Insert your Nanonets Workflow ID in all relevant nodes. Test the Workflow: Enable the workflow and try with a sample document. Features Accepts documents via n8n Form Trigger or direct webhook POST. Securely sends files to Nanonets for document parsing (credentials stored in n8n credentials manager). Automatically waits for async processing, checking Nanonets until results are ready. Extracts both header data and all table/line items into a tabular format. Exports results as an Excel file download. Modular nodes allow easy customization or extension. Prerequisites Nanonets account with workflow configured for your document type. n8n instance with HTTP Request, Webhook/Form, Code, and Excel/Spreadsheet nodes enabled. Valid HTTP Basic Auth credentials saved in n8n for API access. Example Use Cases | Scenario | Benefit | |-----------------------|--------------------------------------------------| | Invoice Processing | Automated extraction of line items and totals | | Receipt Digitization | Parse amounts and charges for expense reports | | Purchase Orders | Convert scanned POs into structured Excel sheets | Notes You must set up credentials in the n8n credentials manager—do not store API keys directly in nodes. All configuration and endpoints are clearly explained with inline sticky notes in the workflow editor. Easily adaptable for other document types or similar APIs—just modify endpoints and result mapping.
By AnuragCompetitor intelligence agent: SERP monitoring + summary with Thordata + OpenAI
Who this is for? This workflow is designed for: Marketing analysts, SEO specialists, and content strategists who want automated intelligence on their online competitors. Growth teams that need quick insights from SERP (Search Engine Results Pages) without manual data scraping. Agencies managing multiple clients’ SEO presence and tracking competitive positioning in real-time. What problem is this workflow solving? Manual competitor research is time-consuming, fragmented, and often lacks actionable insights. This workflow automates the entire process by: Fetching SERP results from multiple search engines (Google, Bing, Yandex, DuckDuckGo) using Thordata’s Scraper API. Using OpenAI GPT-4.1-mini to analyze, summarize, and extract keyword opportunities, topic clusters, and competitor weaknesses. Producing structured, JSON-based insights ready for dashboards or reports. Essentially, it transforms raw SERP data into strategic marketing intelligence — saving hours of research time. What this workflow does Here’s a step-by-step overview of how the workflow operates: Step 1: Manual Trigger Initiates the process on demand when you click “Execute Workflow.” Step 2: Set the Input Query The “Set Input Fields” node defines your search query, such as: > “Top SEO strategies for e-commerce in 2025” Step 3: Multi-Engine SERP Fetching Four HTTP request tools send the query to Thordata Scraper API to retrieve results from: Google Bing Yandex DuckDuckGo Each uses Bearer Authentication configured via “Thordata SERP Bearer Auth Account.” Step 4: AI Agent Processing The LangChain AI Agent orchestrates the data flow, combining inputs and preparing them for structured analysis. Step 5: SEO Analysis The SEO Analyst node (powered by GPT-4.1-mini) parses SERP results into a structured schema, extracting: Competitor domains Page titles & content types Ranking positions Keyword overlaps Traffic share estimations Strengths and weaknesses Step 6: Summarization The Summarize the content node distills complex data into a concise executive summary using GPT-4.1-mini. Step 7: Keyword & Topic Extraction The Keyword and Topic Analysis node extracts: Primary and secondary keywords Topic clusters and content gaps SEO strength scores Competitor insights Step 8: Output Formatting The Structured Output Parser ensures results are clean, validated JSON objects for further integration (e.g., Google Sheets, Notion, or dashboards). Setup Prerequisites n8n Cloud or Self-Hosted instance Thordata Scraper API Key (for SERP data retrieval) OpenAI API Key (for GPT-based reasoning) Setup Steps Add Credentials Go to Credentials → Add New → HTTP Bearer Auth* → Paste your Thordata API token. Add OpenAI API Credentials* for the GPT model. Import the Workflow Copy the provided JSON or upload it into your n8n instance. Set Input In the “Set the Input Fields” node, replace the example query with your desired topic, e.g.: “Google Search for Top SEO strategies for e-commerce in 2025” Execute Click “Execute Workflow” to run the analysis. How to customize this workflow to your needs Modify Search Query Change the search_query variable in the Set Node to any target keyword or topic. Change AI Model In the OpenAI Chat Model nodes, you can switch from gpt-4.1-mini to another model for better quality or lower cost. Extend Analysis Edit the JSON schema in the “Information Extractor” nodes to include: Sentiment analysis of top pages SERP volatility metrics Content freshness indicators Export Results Connect the output to: Google Sheets / Airtable for analytics Notion / Slack for team reporting Webhook / Database for automated storage Summary This workflow creates an AI-powered Competitor Intelligence System inside n8n by blending: Real-time SERP scraping (Thordata) Automated AI reasoning (OpenAI GPT-4.1-mini) Structured data extraction (LangChain Information Extractors)
By Ranjan DailataMonitor customer risk and AI feedback using PostgreSQL, Gmail and Discord
How it works This workflow monitors customer health by combining payment behavior, complaint signals, and AI-driven feedback analysis. It runs on daily and weekly schedules to evaluate risk levels, escalate high-risk customers, and generate structured product insights. High-risk cases are notified instantly, while detailed feedback and audit logs are stored for long-term analysis. Step-by-step Step 1: Triggers & mode selection Daily Risk Check Trigger – Starts the workflow on a daily schedule. Weekly schedule1 – Triggers the workflow for weekly summary runs. Edit Fields3 – Sets flags for daily execution. Edit Fields2 – Sets flags for weekly execution. Switch1 – Routes execution based on daily or weekly mode. Step 2: Risk evaluation & escalation Fetch Customer Risk Data – Pulls customer, payment, product, and complaint data from PostgreSQL. Is High Risk Customer? – Evaluates payment status and complaint count. Prepare Escalation Summary For Low Risk User – Assigns low-risk status and no-action details. Prepare Escalation Summary For High Risk User – Assigns high-risk status and escalation actions. Merge Risk Result – Combines low-risk and high-risk customer records. Send a message4 – Sends the customer risk summary via Gmail. Send a message5 – Sends the same risk summary to Discord. Code in JavaScript3 – Appends notification status and timestamps. Append or update row in sheet3 – Logs risk evaluations and notification status in Google Sheets. Step 3: AI feedback & reporting Get row(s) in sheet1 – Fetches customer records for feedback analysis. Loop Over Items1 – Processes customers one by one. Prompt For Model1 – Builds a structured prompt for product feedback analysis. HTTP Request1 – Sends data to the AI model for insight generation. Code in JavaScript – Merges AI feedback with original customer data. Append or update row in sheet – Stores AI-generated feedback in Google Sheets. Wait1 – Controls execution pacing between records. Merge1 – Prepares consolidated feedback data. Send a message1 – Emails the final AI-powered feedback report. Why use this? Detect customer churn risk early using payment and complaint signals Automatically escalate high-risk customers without manual monitoring Convert raw customer issues into executive-ready product insights Keep a complete audit trail of risk, feedback, and notifications Align support, product, and leadership teams with shared visibility
By Avkash Kakdiya