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.
Lextract extracted 126 fields from Pliant Therapeutics' 100,000 RSF South San Francisco lab lease: $1.4M letter of credit with burn-down schedule, 10-year term.
By Angel Campa, Founder · Updated March 2026
Location
South San Francisco, CA
Size
100,000 RSF
Annual Rent
$6.5M/yr
Term
120 months
Tenant
Pliant Therapeutics
Landlord
ARE-San Francisco No. 63 LLC
This 100,000 RSF lab lease requires a $1.4 million letter of credit as a security deposit — one of the largest in the corpus — and includes a burn-down provision that reduces the LOC amount annually based on tenant performance milestones. Extracting the LOC burn-down schedule and performance triggers is essential for cash management planning.
Lextract extracted the letter of credit amount ($1.4M), identified the burn-down schedule (5 annual reductions tied to no-default milestones), and captured the LC provider requirements and draw conditions. The 10-year term and above-market rent ($65/RSF) were correctly classified as consistent with South San Francisco Class A lab rates.
126
Fields Extracted
Under 3 minutes
Extraction Time
| Field | Extracted Value | Why It Matters |
|---|---|---|
| Security Deposit Type | Letter of Credit | LOC vs cash deposit — affects tenant's balance sheet differently |
| Letter of Credit Amount | $1,400,000 | One of the largest LOCs in portfolio — critical cash management item |
| LOC Burn-Down Schedule | 5 annual reductions to $280K | Burn-down milestones must be tracked to avoid forfeiture |
| Annual Base Rent | $6,500,000 | $65/RSF modified gross — premium lab rate for SSF |
| Lease Term | 120 months (10 years) | Long-term commitment for capital-intensive lab buildout |
| TI Allowance | $150/RSF | $15M allowance for lab-ready buildout — above market |
Lextract identifies the security deposit type (cash, LOC, or both), extracts the amount, and for LOCs also captures the burn-down schedule, required bank, expiration requirements, and draw conditions.
A burn-down provision allows the LOC amount to reduce over time, typically annually, as long as the tenant remains in good standing (no defaults, no bankruptcies). This rewards creditworthy tenants with reduced deposit obligations over time.
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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