Commercial property investors often re-read 60-page leases for every acquisition and renewal decision. An AI-powered OCR pipeline extracts key terms into the CRM with clause citations, turning three hours of abstraction into about eight minutes of verification.
Typical results
22x
Faster than manual
96%
Field-level accuracy
180
Leases in month one
Figures reflect typical results for this kind of automation.
The challenge
Every acquisition brought a data room of scanned leases — sixty pages each, plus amendments that quietly changed the terms. Abstracting one took an analyst three hours, an entire portfolio took weeks, and the summary spreadsheet held no proof of where any number came from. A missed break option or indexation clause could reprice a whole deal.
Before and after
Abstraction time
−96%Leases abstracted
15×Verification per lease
−93%The automation
We built a pipeline where scanned leases drop into a watched folder, get OCR'd, and are read in full by an AI extraction model — amendments and addenda override the original where they conflict. Commencement and expiry dates, rent steps, break options, indexation mechanics, deposits, and guarantees land as structured fields in the CRM. Every field carries a page-and-clause citation, so a human verifies each number in seconds instead of re-reading the lease.
How it works
- 01
Upload
Scanned leases, amendments, and addenda drop into a watched folder — no special formatting needed.
- 02
OCR and read
Documents are OCR'd and read in full by the AI model; amendments override the original where they conflict.
- 03
Terms extracted
Dates, rent steps, break options, indexation, deposits, and guarantees land as structured fields.
- 04
Cited
Every field carries a page-and-clause citation, so verifying a number takes seconds, not a re-read.
- 05
Into the CRM
The abstract lands in the CRM and critical dates schedule their own reminders.
Starts from
- Scanned lease PDFs
- Watched deal folder
Delivers
- Key terms into CRM
- One-page lease brief
- Clause citations to verify
The impact
Lease abstraction typically drops from three hours to eight minutes per document, so a 180-lease portfolio can be processed in about a month instead of a full quarter. Field accuracy runs around 96 percent with every number citable, and break options stop slipping past unnoticed.
Questions about this automation
What lease terms does the extraction capture?
Commencement and expiry dates, rent steps, break options, indexation mechanics, deposits and guarantees, plus any other fields the team defines. Each one lands as a structured field in the CRM. Amendments and addenda in the same folder are read too, and they override the original lease wherever the terms conflict.
How does the team check that the extracted numbers are right?
Every field carries a citation to the page and clause it came from. A reviewer clicks the citation, sees the source text and confirms or corrects the value in seconds. Fields the model is unsure about are marked for review, so the team's attention goes to the values that need it.
What happens after a lease is abstracted?
The abstract is saved to the CRM record for the property and tenant, a one-page lease brief is generated, and critical dates such as break notices and expiries get reminders. Simple automations like this are usually live within days, tested first on a sample of the investor's own leases.