AI-Powered Tenant Screening Summary & Analysis
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Automate the complex process of tenant screening by leveraging AI to quickly summarize key information and provide deep insights from screening reports. This workflow intelligently processes, stores, and analyzes tenant data, logging summarized outcomes for easy review.
About This Workflow
This n8n workflow revolutionizes tenant screening by automating the summarization and analysis of applicant data using advanced AI capabilities. It begins by receiving tenant screening documents via a webhook, intelligently splitting the text, and generating embeddings. These embeddings, along with the original text, are stored in a Pinecone vector database, creating a rich, searchable knowledge base. An AI Agent, powered by a Hugging Face language model, then leverages this knowledge base (through a Retrieval Augmented Generation or RAG approach) and maintains conversational memory to generate concise summaries or answer complex questions about potential tenants. Finally, all generated insights are automatically logged into Google Sheets for streamlined record-keeping and review.
Key Features
- Automated Data Ingestion: Seamlessly ingest tenant screening reports and documents via a dedicated webhook endpoint.
- Intelligent Text Processing: Automatically split large documents into manageable chunks and generate semantic embeddings using Cohere AI.
- Vector Database Knowledge Base: Store and retrieve tenant data efficiently using Pinecone, building a powerful, searchable context layer.
- AI-Powered Summarization & Analysis: Leverage a Hugging Face Large Language Model and an n8n Langchain Agent for sophisticated summarization and insightful analysis.
- Persistent Logging: Automatically log all generated summaries and insights to Google Sheets for compliance, record-keeping, and easy access.
How To Use
- Set up Webhook: Configure the
Webhooknode to receive incoming tenant screening data (e.g., as text content in a POST request body). - Configure AI Services: Provide credentials for
Cohere(for Embeddings),Pinecone(for Vector Store operations), andHuggingFace(for the Chat LLM). - Define Pinecone Index: Ensure your Pinecone account has an index named
tenant_screening_summaryas specified in theInsertandQuerynodes. - Customize Agent Prompt (Optional): Adjust the
Agentnode's prompt to guide the AI on how to summarize or analyze the tenant data based on your specific needs. - Set up Google Sheets: Link your
Google Sheetsnode with the correct credentials and specify thedocumentId(SHEET_ID) andsheetName(Log) where summaries should be appended. - Activate Workflow: Once configured, activate the workflow to automatically process incoming tenant screening requests.
Apps Used
Workflow JSON
{
"id": "d63fafbf-f9d1-4da9-acf7-d40ad595a9ec",
"name": "AI-Powered Tenant Screening Summary & Analysis",
"nodes": 22,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: d63fafbf-f9d1...
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