# Lease abstraction

> Lease abstraction: Terms from the lease: rent, breaks, reviews Checked: proven by code. Produces lease summaries, critical dates.

Source: https://www.sumarity.ai/library/lease-abstraction/

Real estate 

# Lease abstraction

Reads leases (PDF). Produces lease summaries, critical dates. Code works out every figure, the Judgement Engine answers the narrow questions, and whatever stays uncertain goes to a person.

Proven by codeDocuments, onboarding and KYC
Start from this workflowMore for real estate  

What it decides

## Narrow questions, each with a check behind it.

Terms from the lease: rent, breaks, reviews
Proven by code: quotes on the lease. Code proves the answer from the data itself: totals tie, a match is exact, the quote is on the document. Most of these judgements settle without a person.

| Reads | Leases (PDF) 
| Produces | Lease summaries, critical dates 
| Who signs | A person on your team, with every figure traced to its document and every call on record 
| Process | Documents, onboarding and KYC 
| Industry | Real estate    

See it run

## Every line, decided where it's safest.

An example run. Each stage lights up as a line is decided there; pick a stage to see its lines.

ReadsLeases (PDF)CodeReads, matches, ties out and works out every figure.JudgementTerms from the lease: rent, breaks, reviews. Settles only when it clears the cutoff and code proves it.A personGets whatever stays uncertain, with the evidence. Their ruling tunes the next run.ProducesLease summaries, critical dates  
Lease abstraction · example run Example run  

| Ref | Item | Value | Decided by  
| L-12 | Rent: CHF 4,200 a monthQuoted from clause 3.1 | 4,200.00 | Code · quoted 
| L-12 | Break optionTenant break at year 5 · 0.95 · clause 9 |  | Engine · proven 
| L-12 | IndexationCPI, annual · 0.93 |  | Engine · proven 
| L-12 | Service charge capTwo clauses conflict |  | To a person 
| L-12 | Lease end31.12.2031, clause 2 |  | Code · quoted  
|  

Settled 4 To a person 1 Settled wrong 0      Illustrative lines. Amber rows are the Judgement Engine's calls; each settles only above its cutoff and when its check agrees.    

What it could save

## Your volumes in. Your hours out.

In our tests, between 24% and 42% of items still went to a person after tuning. Set your own share; a pilot measures it on your data.

Items per run 
Runs per month 
Minutes per item by hand 
Cost per hour

Share of items that still need a person: 30%  

By hand today80 ha month 
With Sumarity24 ha month, for the items people decide 
Saved56 hCHF 53,760 a year    

Related

## Start from what's closest.

### Documents, onboarding and KYC in other work

Accounting firms

### Client document collection for year-end

What's missing, wrong period, unreadable

Proven by codeAccounting firms

### Client onboarding: AML and engagement checks

Identity, beneficial owners, risk rating

Cross-checkedBanking

### Corporate KYC onboarding

Beneficial owners, documents complete, risk

Cross-checked

### More for real estate

### Rent roll to bank reconciliation

Which receipts pay which tenant's rent

Proven by code

### Service charge (CAM) reconciliation

Costs recoverable and apportioned

Proven by code

### Tenant referencing

Income and references sufficient

Cross-checked  

Questions

## What buyers ask

What happens when Sumarity isn't sure?

Nothing settles below the cutoff, or when the check disagrees. The item goes to a person in the inbox with what the engine saw, its best answer and the runner-up. The person's ruling is kept and tunes the next run.

How are its judgements checked?

Code proves the answer from the data itself: totals tie, a match is exact, the quote is on the document. Most of these judgements settle without a person.

Can we change it to fit how we work?

Yes. Start from this workflow and describe your differences in plain words. The design assistant revises it, the validator checks it, your expert reviews it on the canvas, and your admin publishes it.

Does it read our files as they are?

Yes: spreadsheets and CSV in any layout, PDFs, bank formats and e-mail attachments. Sumarity suggests how each column maps, proves the mapping on a sample and remembers it once a person confirms it.

Where does our data go?

Sumarity runs in Zurich. Your data, backups and logs are stored in Switzerland.

How does it get better?

Your team's rulings become an answer key. Better questions and cutoffs are proposed, tested on data they haven't seen, and published only when your admin approves. It improves itself, with permission.

## Start from this workflow. Run it on your data.

A pilot runs alongside your own process for a few weeks, at our cost, and ends in a line-by-line comparison.

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