How to build a Lease Analyst
An AI-powered lease analysis assistant that parses uploaded lease documents to automatically extract structured metadata, classify lease types, and identify who is responsible for key obligations.
Challenge
Manually reviewing commercial lease documents to extract key terms, dates, financial obligations, and determine lease classification (such as Triple Net/NNN) is time-consuming, error-prone, and requires specialized real estate expertise.
Industry
Real Estate
Department
Sales
Finance
Integrations
OpenAI
This intelligent lease analysis workflow transforms the tedious process of commercial lease review into an automated, structured data extraction pipeline.
How it works:
Document Upload: Users upload lease files (such as PDFs) through the Files node, which processes and parses the document content using OCR and text extraction.
Parallel AI Analysis: The parsed lease content is simultaneously sent to two specialized AI analysts:
NNN Analyst: Determines whether the lease qualifies as a Triple Net (NNN) lease by evaluating who is responsible for property taxes, insurance, and various maintenance categories (roof, HVAC, structural, plumbing, etc.). It outputs a detailed JSON classification with confidence scores and evidence summaries.
Lease Metadata Extractor: Extracts core lease information including landlord/tenant names, property address, critical dates (commencement, expiration, rent start), financial terms (base rent, escalations, security deposit), and key clauses (renewal options, termination rights, permitted use).
Property Address Extraction: A third AI step isolates the property address from the metadata for quick reference.
Structured Outputs: The workflow delivers two outputs—a clean property address and a comprehensive JSON summary of all extracted lease data and classifications.
This workflow is ideal for real estate analysts, property managers, and legal teams who need to quickly assess lease terms and responsibilities without manually combing through lengthy documents.
