AI Lease Abstraction Accuracy: Benchmarks and What to Expect
What accuracy can you realistically expect from AI lease abstraction tools? We break down field-level accuracy rates, where AI excels, where it struggles, and how to validate output.
The designated classification of the space.
By Angel Campa, Founder · Updated March 2026
Property type (office, retail, industrial, medical, etc.) determines which lease clauses are relevant and which market benchmarks apply. CAM structures differ dramatically between retail and office properties, and insurance requirements vary by use type. Misclassifying a property during abstraction can lead to applying incorrect industry standards for evaluating lease terms.
Stated in the lease preamble or "Premises" section. Sometimes implied by the permitted use clause rather than explicitly classified. The lease title itself often indicates the property type (e.g., "Office Lease Agreement" vs. "Retail Lease Agreement").
Lextract uses a combination of AWS Textract OCR and Claude AI to identify and extract the property type from your lease PDF. The AI searches for all pages of the document, then assigns a confidence score based on OCR quality and extraction certainty. Fields with lower confidence are flagged for human review.
Landlord Name
The legal corporate name of the landlord/lessor.
Tenant Name
The legal corporate entity leasing the premises.
Guarantor Name
The entity or individual providing financial backing for the tenant.
Premises Address
The full physical address of the leased space.
Suite/Unit Number
The specific identifier for the tenant's space within a multi-tenant building.
Rentable Area (RSF)
The total area for which the tenant pays rent, including common area allocations.
Absolutely. Retail leases prioritize percentage rent, co-tenancy, and exclusive use provisions. Industrial leases focus on clear height, loading docks, and power capacity. Office leases emphasize base year stops, janitorial services, and after-hours HVAC charges.
The primary types are office, retail, industrial/warehouse, medical, flex/R&D, and mixed-use. Each has distinct lease structures, market benchmarks, and standard clauses that abstractors must understand.
What accuracy can you realistically expect from AI lease abstraction tools? We break down field-level accuracy rates, where AI excels, where it struggles, and how to validate output.
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