Lease abstraction. An illustrative worked example, not a client result: the firm and its numbers are assumptions. Lease abstraction is pulling the terms that matter out of a commercial lease (base rent, escalation clauses, renewal options, key dates) and putting them where your team can act before they lapse. Done by hand across a full portfolio it is slow, so dates get missed and money gets left on the table.
For illustration, picture a commercial property-management firm with a portfolio of 1,000 PDF leases to abstract. That size is an assumption. The work is reading each lease for rent, escalations, renewal options and key dates, then checking them against what the firm actually bills. Nothing here is a measured client result.
#How much analyst time does reviewing a lease portfolio take?
Assume 40 minutes a lease by hand: 1,000 leases is about 670 hours of reading pdfs, most of one person's working year. If an extraction pipeline cut the first read to about 6 minutes, the same portfolio would take about 100 hours. Those are assumptions for the example; a real team would time a sample of its own leases first. The hours handed back would go to the escalations and renewals the team had been too buried to chase.
What is AI for lease abstraction?
AI for lease abstraction reads a commercial lease and extracts the fields a property team acts on: base rent, escalation clauses, renewal options, and key dates. Every field links back to its source clause so a human can verify it. The saving is the cold first read, which a team can time before and after.
#How can AI catch the rent escalations a team is missing?
An escalation is a scheduled rent increase written into the lease. Miss the date and you keep billing last year's rate. The pipeline reads every lease, pulls each escalation clause and its effective date, then checks it against what the firm is actually billing in its system. The mismatches are what there is to recover.
- Base rent and every scheduled escalation, with its effective date
- Renewal and extension options, with the notice window to exercise them
- Key dates: commencement, expiry, and break clauses
- The clause text each field came from, so a human can verify it in one click
Across a portfolio like this, a pipeline would typically surface a cluster of escalations that were never applied, plus renewal options whose notice windows are about to close. That is money already owed, sitting unread in the firm's own files. How much it is depends on the portfolio, and this example makes no claim about an amount.
#Does anything update our property-management system without a person checking it?
No. Designed under AI implementation, the flow is PDF intake, field extraction, rule-based validation, then a human-approval queue. Every extracted field lands in front of an analyst with the source clause beside it. Nothing posts to the property-management system (a Yardi or AppFolio-class platform) until a person clears the exception. That is the shape of our document processing for operations teams.
We are
Stennir builds the extraction-and-approval flow around your existing property-management system and hands your analysts a queue of exceptions to clear.
We aren't
Stennir is not an outsourcing desk you email pdfs to and wait on, and it is not a model that posts rent changes to your books unchecked.
Illustrative scenario: an operations lead discovers that three tenants have been billed last year's rent because the escalations were in the leases the whole time.
This page is an illustrative worked example. No client, firm or figure is behind it, and the numbers are assumptions chosen to show the arithmetic.
If you are sitting on a few hundred leases and a nagging sense that some dates have slipped, that is the shape of work we take. tell us what you're building and we'll scope it with you.
