whitepaper

Why 95% Demo Accuracy Becomes 65% On Your Real Portfolio.

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.

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In depth

Your AI Demo Worked. Your Portfolio Didn't.

Demo accuracy and production accuracy are different numbers: Tools that hit 95% on clean PDFs hit 60-70% on legacy scans, riders, and amendment chains. The drop is the data, not the model.

In shortThe drop is the data, not the model

Wrong answers look right: Confidence stays high while CPI escalation and reconciliation clauses go missing.

Analysts catch some. Most slip past.

In shortMost slip past

The detail

The $2.4M That Was Already Inside The Documents.

A global property services firm needed to process 40,000 legacy leases across 18 markets for a portfolio consolidation.

Manual abstraction would have run $12-24M and taken up to 320,000 analyst hours.

In short000 analyst hours

They deployed AI abstraction with three things in front of it: OCR confidence scoring per document, amendment-to base linkage in chronological order, and clause-level confidence routing to a human review

85% review time reduction.

3x throughput. $2.4M in CPI escalations and operating expense reconciliation rights that the original manual process had never billed because the clauses were buried in rider documents.

In short4M in CPI escalations and operating expense reconcil…

Deep dive

AI Doesn't Generate Lease Revenue. It Finds Revenue You Already Have.

The clauses are already in your documents.

The question is whether your tooling reads them consistently across 40,000 of them, not just the clean ones.

In shortnot just the clean ones

Quality infrastructure routes legacy scans, links amendments in chronological order, and surfaces low confidence extractions to a review queue. The AI gets accurate. Analysts stop reviewing everything.

In shortAnalysts stop reviewing everything

Logiciel builds the preprocessing, linkage, and routing layers that make portfolio-scale lease AI defensible to your finance and legal teams.

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By the numbers

The figures that make it a board-level conversation.

95% → 65%

Demo accuracy vs. legacy-scan accuracy without preprocessing

$2.4M

Escalation revenue recovered from one 40,000-lease portfolio

85%

Manual review time reduction with the right infrastructure

Inside the report

What you'll take away.

OCR Quality Routing

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.

Amendment Linkage

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.

Clause-Level Confidence

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.

Questions

Frequently asked.

Who should read this whitepaper?

Why does demo accuracy drop in production?

What is amendment linkage?

What is clause-level confidence scoring?

Where does the $2.4M actually come from?

Will this work on my portfolio?

How long does deployment take?

Do I need to replace my existing AI vendor?

What teams need to be involved?

How do I get started?

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