Integrate Local File Trigger with 500+ apps and services

This n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts. This assistant can then generate a report to quick flag attention to ensure remedial action is taken. How it works The workflow monitors a local network drive to watch for new bank statements that are added. This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent. The AI Agent analyses the line items to identify the dates and any incoming payments from tenants. The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration. Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts. The final report is generated and logged in the same XLSX for a human to check and action. Requirements A self-hosted version of n8n is required. OpenAI account for the AI model Customising this workflow If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too.

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline. These templates are designed to help a student, associate or clerk quickly summarise, learn and understand the contents to be more productive. Study guide - a short quiz of questions and answered generated by the AI Agent using the contents of the document. Briefing Doc - key information and insights are extracted by the AI into a digestable form. Timeline - key events, durations and people are identified and listed into a simple to understand timeline by the AI How it works A local file trigger watches a local network directory for new documents. New documents are imported into the workflow, its contents extracted and vectorised into a Qdrant vector store to build a mini-knowledgebase. The document then passes through a series of template generating prompts where the AI will perform "research" on the knowledgebase to generate the template contents. Generated study guide, briefing and timeline documents are exported to a designated folder for the user. Requirements Self-hosted version of n8n. Qdrant instance for knowledgebase. Mistral.ai account for embeddings and AI model. Customising your workflow Try adding your own templates or adjusting the existing templates to suit your unique use-case. Anything is quite possible and limited only by your imagination!

If you have a shared or personal drive location with a high frequency of files created by humans, it can become difficult to organise. This may not matter... until you need to search for something! This n8n workflow works with the local filesystem to target the messy folder and categorise as well as organise its files into sub directories automatically. Disclaimer Unfortunately due to the intended use-case, this workflow will not work on n8n Cloud and a self-hosted version of n8n is required. How it works Uses the local file trigger to activate once a new file is introduced to the directory The new file's filename and filetype are analysed using AI to determine the best location to move this file. The AI assess the current subdirectories as to not create duplicates. If a relevant subdirectory is not found, a new subdirectory is suggested. Finally, an Execute Command node uses the AI's suggestions to move the new file into the correct location. Requirements Self-hosted version of n8n. The nodes used in this workflow only work in the self-hosted version. If you are using docker, you must create a bind mount to a host directory. Mistral.ai account for LLM model Customise this workflow If the frequency of files created is high enough, you may not want the trigger to active on every new file created event. Switch to a timer to avoid concurrency issues.