How one global firm recovered $2.4M in escalation revenue across 40,000 leases, and the three infrastructure gaps that separate AI vendor pitches from production results.
Why a 72 DPI scan from 1997 needs a preprocessing pass before extraction reads it, and the confidence-tier routing that decides which documents go to image enhancement first.
How to abstract the lease that exists today, not the one signed ten years ago. Document relationship mapping that applies amendments in order and produces current state output.
Why escalation clauses need a stricter threshold than notice addresses, and how clause-typed review routing produces the 85% review-time reduction the case studies report.
CRE technology leaders, lease admin directors, CFOs, and CTOs at firms running or evaluating commercial lease abstraction AI.
Demo data is clean. Real portfolios contain low-DPI scans, handwritten margins, and amendments that reference clauses by section number rather than restating them. The AI reads what's in front of it.
The layer that assembles the current state of a lease from its base document plus all amendments and riders, applied in chronological order.
Different confidence thresholds for different clause types. Escalation, renewal, and termination clauses get human review at higher thresholds than notice addresses or governing law.
Uncollected CPI escalations, operating expense reconciliation rights, and termination penalties that were buried in rider documents the manual process never abstracted.
Most commercial portfolios have a version of this. The audit tells you whether yours is $50K, $500K, or $5M.
Preprocessing and amendment linkage layers go live in 6-10 weeks for most portfolios. Clause-level confidence tuning takes another 4-6 weeks.
Usually not. The quality infrastructure sits in front of your vendor's extraction layer.
Lease admin, technology, finance, and legal. The audit produces a single accuracy and recovery number each of them can verify.
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